feat(cw): v3 spectrogram-based multi-channel Bayesian decoder
Complete rewrite inspired by Morse Expert / CW Skimmer (VE3NEA): - CwFFT: radix-2 FFT (256-point) for time-frequency analysis - CwSpectrogram: sliding-window waterfall (40 cols x 33 bins, 8ms resolution) - CwBayesianDecoder: Gaussian probability replaces hard dit/dash thresholds - CwChannelTracker: multi-channel peak detection (up to 3 signals) - CwDecoder: integrates all components, monitors 200-1200 Hz simultaneously Key advantages over v2 (ggmorse): - Frequency-agnostic: full spectrum monitored, not locked to one tone - Multi-channel: tracks multiple signals in parallel - Bayesian: probability-based decisions, not hard ratios - Doppler tolerant: frequency drift just moves energy between bins
This commit is contained in:
atsunatsu committed
1 parent
b9e5ff70f5
commit
8af80866bc
7 files changed
+1685
-408
No files matched your search
@@ -0,0 +1,876 @@
|
||||
# CW Decoder v3 — Spectrogram-based Multi-channel Bayesian Decoder
|
||||
|
||||
> **For Hermes:** Use subagent-driven-development skill to implement this plan task-by-task.
|
||||
|
||||
**Goal:** Replace the current time-domain CW decoder with a spectrogram-based multi-channel decoder inspired by Morse Expert / CW Skimmer (VE3NEA). Uses FFT waterfall for frequency-agnostic signal detection and Bayesian probability for symbol timing.
|
||||
|
||||
**Architecture:** Pure Kotlin, no external dependencies. Audio → FFT → Spectrogram → Multi-channel peak detection → Per-channel energy envelope → Bayesian timing analysis → Morse character decoding. Multiple channels tracked simultaneously; the best one is selected for output.
|
||||
|
||||
**Tech Stack:** Kotlin, radix-2 FFT (reuse `Complex` from `SstvDsp`), waterfall spectrogram, Bayesian probability framework.
|
||||
|
||||
---
|
||||
|
||||
## Current Context
|
||||
|
||||
The existing v2 decoder (ggmorse port) has these limitations:
|
||||
- **Single-channel**: locks one Goertzel filter to one frequency
|
||||
- **Time-domain only**: signal disappears if frequency drifts
|
||||
- **Hard thresholds**: dit/dash classification uses fixed ratios
|
||||
- **No probability**: every decision is binary
|
||||
|
||||
**Morse Expert / CW Skimmer approach:**
|
||||
- **Spectrogram-based**: FFT creates a 2D frequency×time matrix
|
||||
- **Multi-channel**: all frequencies monitored simultaneously
|
||||
- **Bayesian**: probability-based decisions, not hard thresholds
|
||||
- **Frequency-agnostic**: drift just moves energy between bins, never lost
|
||||
|
||||
**Key insight from VE3NEA (CW Skimmer author):**
|
||||
> "Instead of making a hard decision at every input sample whether the signal is present or not, compute the probability that the signal is present. Combine that probability with other probabilities using the Bayes formula and pass the new probabilities to the subsequent stages, all the way to the word recognition unit."
|
||||
|
||||
---
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
Audio buffer (128 samples @ 8 kHz)
|
||||
│
|
||||
▼
|
||||
Hanning window
|
||||
│
|
||||
▼
|
||||
FFT (256-point radix-2)
|
||||
│
|
||||
▼
|
||||
Spectrogram update (frequency bins × time columns)
|
||||
│
|
||||
▼
|
||||
Peak detection (find active frequency bins)
|
||||
│
|
||||
▼
|
||||
For each active channel:
|
||||
├─ Energy envelope extraction (time series at that bin)
|
||||
├─ Adaptive noise floor estimation
|
||||
├─ Signal probability computation (Bayesian)
|
||||
└─ Timing analysis → symbol sequence → Morse character
|
||||
│
|
||||
▼
|
||||
Select best channel → Output decoded text
|
||||
```
|
||||
|
||||
**FFT parameters:**
|
||||
- FFT size: 256 points (windowed)
|
||||
- Hop size: 64 samples (75% overlap)
|
||||
- Frequency resolution: 8000/256 = 31.25 Hz per bin
|
||||
- Time resolution: 64/8000 = 8 ms per column
|
||||
- Analyzed range: bins 6-38 (187-1187 Hz, covers 200-1200 Hz CW range)
|
||||
|
||||
---
|
||||
|
||||
## Step-by-step Plan
|
||||
|
||||
### Task 1: Create FFT implementation
|
||||
|
||||
**Objective:** Write a radix-2 FFT that works on real audio data, producing magnitude spectrum.
|
||||
|
||||
**Files:**
|
||||
- Create: `core/domain/src/main/java/.../cw/CwFFT.kt`
|
||||
|
||||
**Code:**
|
||||
```kotlin
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Radix-2 FFT for real-valued input.
|
||||
* Produces magnitude spectrum for the first N/2+1 bins.
|
||||
*/
|
||||
internal class CwFFT(private val n: Int) {
|
||||
init {
|
||||
require(n > 0 && n and (n - 1) == 0) { "FFT size must be power of 2, got $n" }
|
||||
}
|
||||
|
||||
private val cosTable = FloatArray(n / 2)
|
||||
private val sinTable = FloatArray(n / 2)
|
||||
|
||||
init {
|
||||
for (i in 0 until n / 2) {
|
||||
val angle = -2.0 * kotlin.math.PI * i / n
|
||||
cosTable[i] = kotlin.math.cos(angle).toFloat()
|
||||
sinTable[i] = kotlin.math.sin(angle).toFloat()
|
||||
}
|
||||
}
|
||||
|
||||
/** Compute magnitude spectrum for real input. Returns array of size n/2+1. */
|
||||
fun magnitudeSpectrum(input: FloatArray): FloatArray {
|
||||
require(input.size == n) { "Input size must be $n" }
|
||||
|
||||
// Bit-reversal permutation
|
||||
val real = input.copyOf()
|
||||
val imag = FloatArray(n)
|
||||
var j = 0
|
||||
for (i in 1 until n) {
|
||||
var bit = n shr 1
|
||||
while (j and bit != 0) { j = j xor bit; bit = bit shr 1 }
|
||||
j = j xor bit
|
||||
if (i < j) {
|
||||
val tmp = real[i]; real[i] = real[j]; real[j] = tmp
|
||||
}
|
||||
}
|
||||
|
||||
// Radix-2 Cooley-Tukey
|
||||
var len = 2
|
||||
while (len <= n) {
|
||||
val half = len / 2
|
||||
val step = n / len
|
||||
for (i in 0 until n step len) {
|
||||
for (k in 0 until half) {
|
||||
val tReal = real[i + k + half] * cosTable[k * step] - imag[i + k + half] * sinTable[k * step]
|
||||
val tImag = real[i + k + half] * sinTable[k * step] + imag[i + k + half] * cosTable[k * step]
|
||||
real[i + k + half] = real[i + k] - tReal
|
||||
imag[i + k + half] = imag[i + k] - tImag
|
||||
real[i + k] += tReal
|
||||
imag[i + k] += tImag
|
||||
}
|
||||
}
|
||||
len = len shl 1
|
||||
}
|
||||
|
||||
// Magnitude spectrum (first N/2+1 bins)
|
||||
val mag = FloatArray(n / 2 + 1)
|
||||
for (i in 0..n / 2) {
|
||||
mag[i] = kotlin.math.sqrt(real[i] * real[i] + imag[i] * imag[i]) / n
|
||||
}
|
||||
return mag
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Step 1: Verify compilation**
|
||||
|
||||
Run: `./gradlew :core:domain:compileKotlin --no-daemon`
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 2: Commit**
|
||||
|
||||
```bash
|
||||
git add core/domain/src/main/java/.../cw/CwFFT.kt
|
||||
git commit -m "feat(cw): add radix-2 FFT for spectrogram processing"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 2: Create Spectrogram engine
|
||||
|
||||
**Objective:** Maintain a sliding-window spectrogram (frequency×time matrix) that updates with each new audio frame.
|
||||
|
||||
**Files:**
|
||||
- Create: `core/domain/src/main/java/.../cw/CwSpectrogram.kt`
|
||||
|
||||
**Key design:**
|
||||
- Window size: 256 samples, hop 64 samples (75% overlap)
|
||||
- Hanning window applied before FFT
|
||||
- Spectrogram history: 40 columns (40 × 8ms = 320ms, enough for longest Morse dash)
|
||||
- Frequency bins: 6-38 (187-1187 Hz), 33 bins total
|
||||
- Energy normalization: per-bin running average
|
||||
|
||||
```kotlin
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Sliding-window spectrogram for CW decoding.
|
||||
* Maintains a time-frequency matrix updated with each audio frame.
|
||||
*
|
||||
* Parameters:
|
||||
* fftSize = 256, hopSize = 64, sampleRate = 4000
|
||||
* Frequency bins: 6..38 (187-1187 Hz)
|
||||
* History: 40 columns (320 ms window)
|
||||
*/
|
||||
internal class CwSpectrogram(
|
||||
private val fftSize: Int = 256,
|
||||
private val hopSize: Int = 64,
|
||||
private val sampleRate: Int = 4000,
|
||||
private val minBin: Int = 6, // 187 Hz
|
||||
private val maxBin: Int = 38, // 1187 Hz
|
||||
private val historyCols: Int = 40
|
||||
) {
|
||||
private val fft = CwFFT(fftSize)
|
||||
private val numBins = maxBin - minBin + 1
|
||||
private val hanning = FloatArray(fftSize) {
|
||||
(0.5 - 0.5 * kotlin.math.cos(2.0 * kotlin.math.PI * it / (fftSize - 1))).toFloat()
|
||||
}
|
||||
|
||||
// Spectrogram data: [timeCol][freqBin]
|
||||
private val spectrogram = Array(historyCols) { FloatArray(numBins) }
|
||||
private var currentCol = 0
|
||||
private var samplesBuffered = 0
|
||||
private val buffer = FloatArray(fftSize)
|
||||
|
||||
// Per-bin running energy for normalization
|
||||
private val binEnergy = FloatArray(numBins) { 1f }
|
||||
private val alpha = 0.95f
|
||||
|
||||
/** Add audio samples, compute FFTs for each complete hop. */
|
||||
fun addSamples(samples: FloatArray) {
|
||||
var offset = 0
|
||||
while (offset < samples.size) {
|
||||
val needed = fftSize - samplesBuffered
|
||||
val copyLen = kotlin.math.min(needed, samples.size - offset)
|
||||
System.arraycopy(samples, offset, buffer, samplesBuffered, copyLen)
|
||||
samplesBuffered += copyLen
|
||||
offset += copyLen
|
||||
|
||||
if (samplesBuffered >= fftSize) {
|
||||
processFrame()
|
||||
// Shift buffer: keep last (fftSize - hopSize) samples
|
||||
System.arraycopy(buffer, hopSize, buffer, 0, fftSize - hopSize)
|
||||
samplesBuffered = fftSize - hopSize
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private fun processFrame() {
|
||||
// Apply Hanning window
|
||||
val windowed = FloatArray(fftSize) { buffer[it] * hanning[it] }
|
||||
|
||||
// Compute FFT magnitude spectrum
|
||||
val mag = fft.magnitudeSpectrum(windowed)
|
||||
|
||||
// Update spectrogram column
|
||||
val col = spectrogram[currentCol]
|
||||
for (b in 0 until numBins) {
|
||||
val binIdx = minBin + b
|
||||
val rawMag = mag[binIdx]
|
||||
// Running energy normalization
|
||||
binEnergy[b] = alpha * binEnergy[b] + (1 - alpha) * rawMag
|
||||
col[b] = if (binEnergy[b] > 1e-6f) rawMag / binEnergy[b] else 0f
|
||||
}
|
||||
|
||||
currentCol = (currentCol + 1) % historyCols
|
||||
}
|
||||
|
||||
/** Get the current spectrogram as a 2D array. */
|
||||
fun getSpectrogram(): Array<FloatArray> {
|
||||
// Return in chronological order
|
||||
val result = Array(historyCols) { i ->
|
||||
val srcIdx = (currentCol + i) % historyCols
|
||||
spectrogram[srcIdx].copyOf()
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
/** Get the most recent column (current energy across all frequencies). */
|
||||
fun getCurrentColumn(): FloatArray {
|
||||
val prevCol = (currentCol - 1 + historyCols) % historyCols
|
||||
return spectrogram[prevCol].copyOf()
|
||||
}
|
||||
|
||||
/** Find the frequency bin with peak energy. */
|
||||
fun findPeakBin(): Int {
|
||||
val col = getCurrentColumn()
|
||||
var maxBin = 0
|
||||
var maxVal = 0f
|
||||
for (i in col.indices) {
|
||||
if (col[i] > maxVal) {
|
||||
maxVal = col[i]
|
||||
maxBin = i
|
||||
}
|
||||
}
|
||||
return if (maxVal > 0.3f) maxBin else -1
|
||||
}
|
||||
|
||||
/** Get energy at a specific bin over the last N columns. */
|
||||
fun getBinEnergy(bin: Int, numCols: Int): FloatArray {
|
||||
val result = FloatArray(kotlin.math.min(numCols, historyCols))
|
||||
for (i in result.indices) {
|
||||
val colIdx = (currentCol - 1 - i + historyCols) % historyCols
|
||||
result[result.size - 1 - i] = spectrogram[colIdx][bin]
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
/** Get the bin index for a frequency in Hz. */
|
||||
fun freqToBin(freqHz: Float): Int {
|
||||
val bin = (freqHz * fftSize / sampleRate).toInt()
|
||||
return (bin - minBin).coerceIn(0, numBins - 1)
|
||||
}
|
||||
|
||||
/** Get the center frequency for a bin. */
|
||||
fun binToFreq(bin: Int): Float {
|
||||
return (minBin + bin).toFloat() * sampleRate / fftSize
|
||||
}
|
||||
|
||||
fun reset() {
|
||||
for (col in spectrogram) col.fill(0f)
|
||||
currentCol = 0
|
||||
samplesBuffered = 0
|
||||
buffer.fill(0f)
|
||||
binEnergy.fill(1f)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Step 1: Verify compilation**
|
||||
|
||||
Run: `./gradlew :core:domain:compileKotlin --no-daemon`
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 2: Commit**
|
||||
|
||||
```bash
|
||||
git add core/domain/src/main/java/.../cw/CwSpectrogram.kt
|
||||
git commit -m "feat(cw): add sliding-window spectrogram for multi-channel CW detection"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 3: Create Bayesian timing decoder
|
||||
|
||||
**Objective:** Implement a probability-based Morse timing analyzer that replaces hard thresholds with Bayesian probability distributions.
|
||||
|
||||
**Files:**
|
||||
- Create: `core/domain/src/main/java/.../cw/CwBayesianDecoder.kt`
|
||||
|
||||
**Bayesian approach:**
|
||||
- Instead of "is this a dit or a dash?" (hard decision)
|
||||
- Compute: P(dit | duration), P(dash | duration), P(gap | duration)
|
||||
- Use observed timing distributions as prior probabilities
|
||||
- Layer probabilities: signal presence → symbol type → character → word
|
||||
|
||||
```kotlin
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Bayesian Morse timing decoder.
|
||||
* Replaces hard thresholds with probability-based decision making.
|
||||
*
|
||||
* Instead of:
|
||||
* if (ratio < 1.5) → dit
|
||||
* else if (ratio < 5.0) → dash
|
||||
*
|
||||
* We compute:
|
||||
* P(dit | duration) = P(duration | dit) * P(dit) / P(duration)
|
||||
* P(dash | duration) = P(duration | dash) * P(dash) / P(duration)
|
||||
*
|
||||
* And pick the most likely interpretation.
|
||||
*/
|
||||
internal class CwBayesianDecoder {
|
||||
|
||||
// Morse timing parameters (will be learned from signal)
|
||||
private var dotDurationMs = 60f // initial 20 WPM
|
||||
private var speedWpm = 20f
|
||||
private var pitchHz = 700f
|
||||
|
||||
// Symbol history for Bayesian inference
|
||||
private val recentDits = mutableListOf<Float>()
|
||||
private val recentDashes = mutableListOf<Float>()
|
||||
private val recentGaps = mutableListOf<Float>()
|
||||
|
||||
// Current symbol being accumulated
|
||||
private var currentSymbol = StringBuilder()
|
||||
private var decodedText = StringBuilder()
|
||||
|
||||
// Output
|
||||
private var _decodedText = ""
|
||||
val decodedText: String get() = _decodedText
|
||||
|
||||
/**
|
||||
* Compute probability that a duration matches a Morse element type.
|
||||
* Uses Gaussian probability density centered on the expected duration.
|
||||
*/
|
||||
private fun probabilityOf(durationMs: Float, expectedMs: Float, varianceMs: Float): Float {
|
||||
if (varianceMs <= 0f) return 0f
|
||||
val diff = durationMs - expectedMs
|
||||
return kotlin.math.exp(-(diff * diff) / (2 * varianceMs * varianceMs))
|
||||
}
|
||||
|
||||
/**
|
||||
* Process a tone (signal present) duration in milliseconds.
|
||||
* Returns the most likely symbol type and its probability.
|
||||
*/
|
||||
fun processTone(durationMs: Float): SymbolResult {
|
||||
val ditProb = probabilityOf(durationMs, dotDurationMs, dotDurationMs * 0.3f)
|
||||
val dashProb = probabilityOf(durationMs, dotDurationMs * 3, dotDurationMs * 0.5f)
|
||||
|
||||
if (ditProb > dashProb && ditProb > 0.1f) {
|
||||
currentSymbol.append('.')
|
||||
recentDits.add(durationMs)
|
||||
updateSpeedEstimate()
|
||||
return SymbolResult('.', ditProb)
|
||||
} else if (dashProb > 0.1f) {
|
||||
currentSymbol.append('-')
|
||||
recentDashes.add(durationMs)
|
||||
return SymbolResult('-', dashProb)
|
||||
}
|
||||
return SymbolResult(null, 0f)
|
||||
}
|
||||
|
||||
/**
|
||||
* Process a gap (silence) duration in milliseconds.
|
||||
* Determines if it's intra-char, inter-char, or word gap.
|
||||
* Returns the decoded character if a complete symbol was decoded.
|
||||
*/
|
||||
fun processGap(durationMs: Float): Char? {
|
||||
if (currentSymbol.isEmpty()) {
|
||||
// No symbol in progress — could be a word gap
|
||||
val wordGapProb = probabilityOf(durationMs, dotDurationMs * 7, dotDurationMs * 1.0f)
|
||||
if (wordGapProb > 0.3f) {
|
||||
decodedText.append(' ')
|
||||
_decodedText = decodedText.toString()
|
||||
return ' '
|
||||
}
|
||||
return null
|
||||
}
|
||||
|
||||
// Inter-char gap vs intra-char gap
|
||||
val interCharProb = probabilityOf(durationMs, dotDurationMs * 3, dotDurationMs * 0.5f)
|
||||
val intraCharProb = probabilityOf(durationMs, dotDurationMs * 1, dotDurationMs * 0.3f)
|
||||
val wordGapProb = probabilityOf(durationMs, dotDurationMs * 7, dotDurationMs * 1.0f)
|
||||
|
||||
return when {
|
||||
wordGapProb > interCharProb && wordGapProb > 0.3f -> {
|
||||
val char = decodeCurrentSymbol()
|
||||
decodedText.append(' ')
|
||||
_decodedText = decodedText.toString()
|
||||
char
|
||||
}
|
||||
interCharProb > intraCharProb && interCharProb > 0.2f -> {
|
||||
val char = decodeCurrentSymbol()
|
||||
_decodedText = decodedText.toString()
|
||||
char
|
||||
}
|
||||
else -> null // intra-char gap, continue building symbol
|
||||
}
|
||||
}
|
||||
|
||||
private fun decodeCurrentSymbol(): Char? {
|
||||
if (currentSymbol.isEmpty()) return null
|
||||
val morse = currentSymbol.toString()
|
||||
currentSymbol.clear()
|
||||
val char = morseToChar(morse)
|
||||
if (char != null) {
|
||||
decodedText.append(char)
|
||||
}
|
||||
return char
|
||||
}
|
||||
|
||||
private fun updateSpeedEstimate() {
|
||||
if (recentDits.size < 3) return
|
||||
val sorted = recentDits.sorted()
|
||||
val median = sorted[sorted.size / 2]
|
||||
if (median > 0f) {
|
||||
dotDurationMs = dotDurationMs * 0.7f + median * 0.3f
|
||||
speedWpm = 60.0f / (50.0f * dotDurationMs / 1000.0f)
|
||||
}
|
||||
}
|
||||
|
||||
fun setPitch(pitch: Float) { pitchHz = pitch }
|
||||
|
||||
fun getSpeed(): Float = speedWpm
|
||||
|
||||
fun reset() {
|
||||
dotDurationMs = 60f
|
||||
speedWpm = 20f
|
||||
recentDits.clear()
|
||||
recentDashes.clear()
|
||||
recentGaps.clear()
|
||||
currentSymbol.clear()
|
||||
decodedText.clear()
|
||||
_decodedText = ""
|
||||
}
|
||||
|
||||
companion object {
|
||||
private val MORSE_TABLE = mapOf(
|
||||
"01" to 'A', "1000" to 'B', "1010" to 'C', "100" to 'D', "0" to 'E',
|
||||
"0010" to 'F', "110" to 'G', "0000" to 'H', "00" to 'I', "0111" to 'J',
|
||||
"101" to 'K', "0100" to 'L', "11" to 'M', "10" to 'N', "111" to 'O',
|
||||
"0110" to 'P', "1101" to 'Q', "010" to 'R', "000" to 'S', "1" to 'T',
|
||||
"001" to 'U', "0001" to 'V', "011" to 'W', "1001" to 'X', "1011" to 'Y',
|
||||
"1100" to 'Z', "01111" to '1', "00111" to '2', "00011" to '3',
|
||||
"00001" to '4', "00000" to '5', "10000" to '6', "11000" to '7',
|
||||
"11100" to '8', "11110" to '9', "11111" to '0',
|
||||
"010101" to '.', "110011" to ',', "001100" to '?', "011110" to '\'',
|
||||
"101011" to '!', "10010" to '/', "10110" to '(', "101101" to ')',
|
||||
"01000" to '&', "111000" to ':', "101010" to ';', "10001" to '=',
|
||||
"01010" to '+', "100001" to '-', "001101" to '_', "010010" to '"',
|
||||
"0001001" to '$', "011010" to '@'
|
||||
)
|
||||
fun morseToChar(morse: String): Char? = MORSE_TABLE[morse]
|
||||
}
|
||||
}
|
||||
|
||||
data class SymbolResult(val symbol: Char?, val probability: Float)
|
||||
```
|
||||
|
||||
**Step 1: Verify compilation**
|
||||
|
||||
Run: `./gradlew :core:domain:compileKotlin --no-daemon`
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 2: Commit**
|
||||
|
||||
```bash
|
||||
git add core/domain/src/main/java/.../cw/CwBayesianDecoder.kt
|
||||
git commit -m "feat(cw): add Bayesian probability-based timing decoder"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 4: Build multi-channel signal tracker
|
||||
|
||||
**Objective:** Create a channel tracker that monitors multiple frequency bins, extracts energy envelopes, and feeds the best one to the Bayesian decoder.
|
||||
|
||||
**Files:**
|
||||
- Create: `core/domain/src/main/java/.../cw/CwChannelTracker.kt`
|
||||
|
||||
**Design:**
|
||||
- Scan spectrogram for active frequency bins (energy > threshold)
|
||||
- For each active bin, extract the energy envelope over time
|
||||
- Track up to 3 channels simultaneously
|
||||
- For each channel, compute signal presence probability
|
||||
- Select the channel with highest confidence for output
|
||||
|
||||
```kotlin
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Multi-channel CW signal tracker.
|
||||
* Monitors the spectrogram for active frequency bins and extracts
|
||||
* energy envelopes for each detected signal.
|
||||
*/
|
||||
internal class CwChannelTracker(
|
||||
private val spectrogram: CwSpectrogram,
|
||||
private val numChannels: Int = 3
|
||||
) {
|
||||
data class Channel(
|
||||
val bin: Int,
|
||||
val frequency: Float,
|
||||
var active: Boolean = false,
|
||||
var energy: Float = 0f,
|
||||
var history: MutableList<Float> = mutableListOf(),
|
||||
var confidence: Float = 0f
|
||||
)
|
||||
|
||||
private val channels = Array(numChannels) { Channel(0, 0f) }
|
||||
private var activeCount = 0
|
||||
|
||||
/** Scan spectrogram and update channel tracking. */
|
||||
fun update(): List<Channel> {
|
||||
val col = spectrogram.getCurrentColumn()
|
||||
val peaks = findPeaks(col, threshold = 0.3f, minDistance = 2)
|
||||
|
||||
// Update existing channels
|
||||
for (ch in channels) {
|
||||
if (ch.active) {
|
||||
// Check if this bin is still active
|
||||
if (peaks.contains(ch.bin)) {
|
||||
ch.energy = col[ch.bin]
|
||||
ch.history.add(ch.energy)
|
||||
if (ch.history.size > 40) ch.history.removeAt(0)
|
||||
ch.confidence = computeConfidence(ch.history)
|
||||
} else {
|
||||
// Signal lost — keep for a few frames then deactivate
|
||||
ch.history.add(0f)
|
||||
if (ch.history.size > 40) ch.history.removeAt(0)
|
||||
ch.confidence *= 0.9f
|
||||
if (ch.confidence < 0.1f) ch.active = false
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Assign new peaks to inactive channels
|
||||
var peakIdx = 0
|
||||
for (ch in channels) {
|
||||
if (!ch.active && peakIdx < peaks.size) {
|
||||
val bin = peaks[peakIdx]
|
||||
ch.bin = bin
|
||||
ch.frequency = spectrogram.binToFreq(bin)
|
||||
ch.active = true
|
||||
ch.energy = col[bin]
|
||||
ch.history.clear()
|
||||
ch.confidence = 0.5f
|
||||
peakIdx++
|
||||
}
|
||||
}
|
||||
|
||||
activeCount = channels.count { it.active }
|
||||
return channels.filter { it.active }
|
||||
}
|
||||
|
||||
/** Find peak bins in the current spectrum. */
|
||||
private fun findPeaks(spectrum: FloatArray, threshold: Float, minDistance: Int): List<Int> {
|
||||
val peaks = mutableListOf<Int>()
|
||||
for (i in 1 until spectrum.size - 1) {
|
||||
if (spectrum[i] > spectrum[i - 1] && spectrum[i] > spectrum[i + 1] && spectrum[i] > threshold) {
|
||||
// Check minimum distance from existing peaks
|
||||
if (peaks.isEmpty() || i - peaks.last() >= minDistance) {
|
||||
peaks.add(i)
|
||||
}
|
||||
}
|
||||
}
|
||||
peaks.sortByDescending { spectrum[it] }
|
||||
return peaks
|
||||
}
|
||||
|
||||
/** Compute confidence score from energy history. */
|
||||
private fun computeConfidence(history: List<Float>): Float {
|
||||
if (history.size < 10) return 0.3f
|
||||
val recent = history.takeLast(10)
|
||||
val mean = recent.average().toFloat()
|
||||
val variance = recent.map { (it - mean) * (it - mean) }.average().toFloat()
|
||||
// Lower variance = more stable signal = higher confidence
|
||||
return if (mean > 0f) (mean / (mean + variance + 0.1f)).coerceIn(0f, 1f) else 0f
|
||||
}
|
||||
|
||||
/** Get the best channel (highest confidence). */
|
||||
fun getBestChannel(): Channel? {
|
||||
return channels.filter { it.active }.maxByOrNull { it.confidence }
|
||||
}
|
||||
|
||||
fun reset() {
|
||||
for (ch in channels) {
|
||||
ch.bin = 0; ch.frequency = 0f; ch.active = false
|
||||
ch.energy = 0f; ch.history.clear(); ch.confidence = 0f
|
||||
}
|
||||
activeCount = 0
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Step 1: Verify compilation**
|
||||
|
||||
Run: `./gradlew :core:domain:compileKotlin --no-daemon`
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 2: Commit**
|
||||
|
||||
```bash
|
||||
git add core/domain/src/main/java/.../cw/CwChannelTracker.kt
|
||||
git commit -m "feat(cw): add multi-channel signal tracker for spectrogram peak detection"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 5: Integrate into new CwDecoder
|
||||
|
||||
**Objective:** Replace the existing CwDecoder with the new spectrogram-based multi-channel decoder.
|
||||
|
||||
**Files:**
|
||||
- Modify: `core/domain/src/main/java/.../cw/CwDecoder.kt` (complete rewrite)
|
||||
- Delete (optional): `CwResampler.kt`, `CwFilter.kt`, `CwGoertzel.kt`, `CwSTFFT.kt` (no longer needed)
|
||||
|
||||
**New CwDecoder flow:**
|
||||
```
|
||||
processBuffer(buffer):
|
||||
1. Feed samples to spectrogram
|
||||
2. Update channel tracker
|
||||
3. For each active channel:
|
||||
a. Extract energy envelope
|
||||
b. Detect signal presence (with Bayesian probability)
|
||||
c. Measure tone/gap durations
|
||||
d. Feed to Bayesian decoder
|
||||
4. Select best channel's output
|
||||
```
|
||||
|
||||
```kotlin
|
||||
class CwDecoder(
|
||||
val sampleRate: Int = 8000
|
||||
) {
|
||||
private val spectrogram = CwSpectrogram(
|
||||
fftSize = 256, hopSize = 64,
|
||||
sampleRate = sampleRate, minBin = 6, maxBin = 38
|
||||
)
|
||||
private val channelTracker = CwChannelTracker(spectrogram)
|
||||
private val bayesianDecoder = CwBayesianDecoder()
|
||||
|
||||
// Per-channel state tracking
|
||||
private data class ChannelState(
|
||||
var isSignal: Boolean = false,
|
||||
var toneSamples: Int = 0,
|
||||
var gapSamples: Int = 0,
|
||||
var lastThreshold: Float = 0f
|
||||
)
|
||||
private val channelStates = Array(3) { ChannelState() }
|
||||
|
||||
// Output flows
|
||||
private val _decodedTextFlow = MutableStateFlow("")
|
||||
val decodedTextFlow: StateFlow<String> = _decodedTextFlow
|
||||
private val _signalStrength = MutableStateFlow(0f)
|
||||
val signalStrength: StateFlow<Float> = _signalStrength
|
||||
private val _estimatedPitch = MutableStateFlow<Float?>(null)
|
||||
val estimatedPitch: StateFlow<Float?> = _estimatedPitch
|
||||
private val _estimatedSpeed = MutableStateFlow<Float?>(null)
|
||||
val estimatedSpeed: StateFlow<Float?> = _estimatedSpeed
|
||||
|
||||
// Timing: 1 sample at 4 kHz = 0.25 ms
|
||||
private val samplePeriodMs = 1000f / 4000 // 0.25 ms
|
||||
|
||||
fun processBuffer(buffer: FloatArray) {
|
||||
// 1. Update spectrogram
|
||||
spectrogram.addSamples(buffer)
|
||||
|
||||
// 2. Update channel tracker
|
||||
val activeChannels = channelTracker.update()
|
||||
|
||||
// 3. Process each active channel
|
||||
for ((idx, channel) in activeChannels.withIndex()) {
|
||||
if (idx >= channelStates.size) break
|
||||
val state = channelStates[idx]
|
||||
val col = spectrogram.getCurrentColumn()
|
||||
val energy = if (channel.bin in col.indices) col[channel.bin] else 0f
|
||||
|
||||
// Adaptive threshold for this channel
|
||||
val noiseFloor = 0.3f
|
||||
val threshold = noiseFloor + (energy - noiseFloor) * 0.3f
|
||||
state.lastThreshold = threshold
|
||||
|
||||
// Signal present?
|
||||
if (energy > threshold) {
|
||||
if (!state.isSignal) {
|
||||
// Rising edge — process gap
|
||||
if (state.gapSamples > 0) {
|
||||
val gapMs = state.gapSamples * samplePeriodMs
|
||||
bayesianDecoder.processGap(gapMs)
|
||||
}
|
||||
state.gapSamples = 0
|
||||
state.isSignal = true
|
||||
}
|
||||
state.toneSamples++
|
||||
} else {
|
||||
if (state.isSignal) {
|
||||
// Falling edge — process tone
|
||||
val toneMs = state.toneSamples * samplePeriodMs
|
||||
bayesianDecoder.processTone(toneMs)
|
||||
state.toneSamples = 0
|
||||
state.isSignal = false
|
||||
}
|
||||
state.gapSamples++
|
||||
}
|
||||
}
|
||||
|
||||
// 4. Update output from best channel
|
||||
val bestChannel = channelTracker.getBestChannel()
|
||||
if (bestChannel != null) {
|
||||
_estimatedPitch.value = bestChannel.frequency
|
||||
_signalStrength.value = bestChannel.confidence
|
||||
_estimatedSpeed.value = bayesianDecoder.getSpeed()
|
||||
}
|
||||
_decodedTextFlow.value = bayesianDecoder.decodedText
|
||||
}
|
||||
|
||||
fun resetDecoder() {
|
||||
spectrogram.reset()
|
||||
channelTracker.reset()
|
||||
bayesianDecoder.reset()
|
||||
for (state in channelStates) {
|
||||
state.isSignal = false; state.toneSamples = 0
|
||||
state.gapSamples = 0; state.lastThreshold = 0f
|
||||
}
|
||||
_decodedTextFlow.value = ""
|
||||
_signalStrength.value = 0f
|
||||
_estimatedPitch.value = null
|
||||
_estimatedSpeed.value = null
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Step 1: Verify compilation**
|
||||
|
||||
Run: `./gradlew :core:domain:compileKotlin --no-daemon`
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 2: Commit**
|
||||
|
||||
```bash
|
||||
git add core/domain/src/main/java/.../cw/CwDecoder.kt
|
||||
git commit -m "feat(cw): replace with spectrogram-based multi-channel Bayesian decoder"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 6: Rewrite unit tests
|
||||
|
||||
**Objective:** Update tests to cover the new architecture — FFT, spectrogram, Bayesian probability, multi-channel tracking.
|
||||
|
||||
**Files:**
|
||||
- Modify: `core/domain/src/test/java/.../cw/CwDecoderTest.kt`
|
||||
|
||||
**Key test additions:**
|
||||
- `fft_magnitudeSpectrum_detectsTone()` — generate 700 Hz tone, assert peak at correct bin
|
||||
- `fft_magnitudeSpectrum_silence_isFlat()` — all-zero input, flat spectrum
|
||||
- `spectrogram_addSamples_updatesEnergy()` — single tone increases energy at its bin
|
||||
- `spectrogram_findPeakBin_returnsCorrectBin()` — strongest tone found
|
||||
- `spectrogram_binToFreq_roundtrip()` — freq→bin→freq is consistent
|
||||
- `bayesian_processTone_dit()` — short tone produces dit
|
||||
- `bayesian_processTone_dash()` — 3x tone produces dash
|
||||
- `bayesian_processGap_interChar()` — gap produces character
|
||||
- `bayesian_processGap_wordGap()` — long gap adds space
|
||||
- `channelTracker_update_createsChannels()` — single tone creates one channel
|
||||
- `channelTracker_findPeaks_multipleTones()` — multiple tones create multiple channels
|
||||
- `decoder_fullPipeline()` — end-to-end silence→no crash
|
||||
|
||||
**Step 1: Run tests**
|
||||
|
||||
Run: `./gradlew :core:domain:test --no-daemon`
|
||||
Expected: BUILD SUCCESSFUL, 15+ tests pass
|
||||
|
||||
**Step 2: Commit**
|
||||
|
||||
```bash
|
||||
git add core/domain/src/test/java/.../cw/CwDecoderTest.kt
|
||||
git commit -m "test(cw): add tests for spectrogram, FFT, Bayesian decoder, channel tracker"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 7: Full build verification
|
||||
|
||||
**Objective:** Ensure the app compiles and all tests pass.
|
||||
|
||||
**Step 1: Build debug APK**
|
||||
|
||||
```bash
|
||||
./gradlew :app:assembleDebug --no-daemon
|
||||
```
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 2: Run all domain tests**
|
||||
|
||||
```bash
|
||||
./gradlew :core:domain:test --no-daemon
|
||||
```
|
||||
Expected: BUILD SUCCESSFUL
|
||||
|
||||
**Step 3: Commit and push**
|
||||
|
||||
```bash
|
||||
git add -A
|
||||
git commit -m "feat(cw): v3 spectrogram-based multi-channel Bayesian decoder"
|
||||
git push fork main
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Files changed
|
||||
|
||||
| File | Action |
|
||||
|------|--------|
|
||||
| `core/domain/src/main/java/.../cw/CwFFT.kt` | Create |
|
||||
| `core/domain/src/main/java/.../cw/CwSpectrogram.kt` | Create |
|
||||
| `core/domain/src/main/java/.../cw/CwBayesianDecoder.kt` | Create |
|
||||
| `core/domain/src/main/java/.../cw/CwChannelTracker.kt` | Create |
|
||||
| `core/domain/src/main/java/.../cw/CwDecoder.kt` | Rewrite |
|
||||
| `core/domain/src/main/java/.../cw/CwResampler.kt` | Keep (unused, can delete) |
|
||||
| `core/domain/src/main/java/.../cw/CwFilter.kt` | Keep (unused, can delete) |
|
||||
| `core/domain/src/main/java/.../cw/CwGoertzel.kt` | Keep (unused, can delete) |
|
||||
| `core/domain/src/main/java/.../cw/CwSTFFT.kt` | Keep (unused, can delete) |
|
||||
| `core/domain/src/test/java/.../cw/CwDecoderTest.kt` | Rewrite |
|
||||
|
||||
## Risks / tradeoffs
|
||||
|
||||
- **CPU cost:** FFT every 64 samples at 4 kHz = 62.5 FFTs/second. 256-point radix-2 FFT is ~2,500 float ops. On a modern phone this is negligible (< 0.1% CPU).
|
||||
- **Memory:** Spectrogram = 40 cols × 33 bins × 4 bytes = ~5 KB. Trivial.
|
||||
- **Frequency resolution:** 31.25 Hz per bin. Fine enough for CW (typical tone stability is ±10 Hz, but ±50 Hz is still fine).
|
||||
- **Time resolution:** 8 ms per column. At 20 WPM, a dit is 60 ms = 7.5 columns. Enough for accurate timing.
|
||||
- **Multi-channel complexity:** Current implementation tracks up to 3 channels. In practice, the strongest signal is usually the desired one. The channel tracker handles this by selecting highest confidence.
|
||||
- **Bayesian advantage:** The probability-based approach naturally handles ambiguous timing. A tone that's between dit and dash length won't be forced into a wrong category — it'll have low probability for both, and the decoder can wait for more context.
|
||||
- **Backward compatibility:** `CwDecoder` class name and public interface unchanged. UI/ViewModel work without modification.
|
||||
- **Noise performance:** The normalized spectrogram (per-bin energy tracking) naturally handles varying noise floors. A signal that's 2x above the noise floor at its bin will be detected regardless of absolute level.
|
||||
@@ -0,0 +1,154 @@
|
||||
/*
|
||||
* Look4Sat. Amateur radio satellite tracker and pass predictor.
|
||||
* Copyright (C) 2019-2026 Arty Bishop and contributors.
|
||||
*
|
||||
* This program is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
*/
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Bayesian Morse timing decoder.
|
||||
* Replaces hard thresholds with probability-based decision making.
|
||||
*
|
||||
* Inspired by VE3NEA's CW Skimmer approach:
|
||||
* "Instead of making a hard decision at every input sample whether the signal
|
||||
* is present or not, compute the probability that the signal is present."
|
||||
*
|
||||
* Uses Gaussian probability density centered on expected durations:
|
||||
* P(dit | duration) = exp(-(duration - dotMs)^2 / (2 * variance^2))
|
||||
* P(dash | duration) = exp(-(duration - 3*dotMs)^2 / (2 * variance^2))
|
||||
*/
|
||||
internal class CwBayesianDecoder {
|
||||
|
||||
// Morse timing parameters
|
||||
private var dotDurationMs = 60f // initial 20 WPM
|
||||
private var speedWpm = 20f
|
||||
|
||||
// Current symbol being accumulated
|
||||
private var currentSymbol = StringBuilder()
|
||||
private var textBuffer = StringBuilder()
|
||||
|
||||
// Recent dit lengths for speed estimation
|
||||
private val recentDits = mutableListOf<Float>()
|
||||
|
||||
// Output
|
||||
private var _decodedText = ""
|
||||
val decodedText: String get() = _decodedText
|
||||
|
||||
/** Gaussian probability. */
|
||||
private fun gaussianProb(durationMs: Float, expectedMs: Float, varianceMs: Float): Float {
|
||||
if (varianceMs <= 0f) return 0f
|
||||
val diff = durationMs - expectedMs
|
||||
return kotlin.math.exp(-(diff * diff) / (2 * varianceMs * varianceMs))
|
||||
}
|
||||
|
||||
/** Process a tone duration. Returns the symbol type with highest probability. */
|
||||
fun processTone(durationMs: Float): ToneResult {
|
||||
val ditProb = gaussianProb(durationMs, dotDurationMs, dotDurationMs * 0.4f)
|
||||
val dashProb = gaussianProb(durationMs, dotDurationMs * 3f, dotDurationMs * 0.6f)
|
||||
|
||||
return if (ditProb > dashProb && ditProb > 0.05f) {
|
||||
currentSymbol.append('0')
|
||||
recentDits.add(durationMs)
|
||||
updateSpeed()
|
||||
ToneResult('0', ditProb)
|
||||
} else if (dashProb > 0.05f) {
|
||||
currentSymbol.append('1')
|
||||
ToneResult('1', dashProb)
|
||||
} else {
|
||||
ToneResult(null, 0f)
|
||||
}
|
||||
}
|
||||
|
||||
/** Process a gap duration. Returns decoded character or null. */
|
||||
fun processGap(durationMs: Float): Char? {
|
||||
if (currentSymbol.isEmpty()) {
|
||||
val wordProb = gaussianProb(durationMs, dotDurationMs * 7f, dotDurationMs * 1.2f)
|
||||
if (wordProb > 0.2f) {
|
||||
textBuffer.append(' ')
|
||||
_decodedText = textBuffer.toString()
|
||||
return ' '
|
||||
}
|
||||
return null
|
||||
}
|
||||
|
||||
val interCharProb = gaussianProb(durationMs, dotDurationMs * 3f, dotDurationMs * 0.6f)
|
||||
val wordProb = gaussianProb(durationMs, dotDurationMs * 7f, dotDurationMs * 1.2f)
|
||||
|
||||
if (wordProb > interCharProb && wordProb > 0.2f) {
|
||||
val char = flushSymbol()
|
||||
textBuffer.append(' ')
|
||||
_decodedText = textBuffer.toString()
|
||||
return char
|
||||
}
|
||||
if (interCharProb > 0.15f) {
|
||||
val char = flushSymbol()
|
||||
_decodedText = textBuffer.toString()
|
||||
return char
|
||||
}
|
||||
return null
|
||||
}
|
||||
|
||||
private fun flushSymbol(): Char? {
|
||||
if (currentSymbol.isEmpty()) return null
|
||||
val morse = currentSymbol.toString()
|
||||
currentSymbol.clear()
|
||||
val char = morseToChar(morse)
|
||||
if (char != null) textBuffer.append(char)
|
||||
return char
|
||||
}
|
||||
|
||||
private fun updateSpeed() {
|
||||
if (recentDits.size < 3) return
|
||||
val sorted = recentDits.sorted()
|
||||
val median = sorted[sorted.size / 2]
|
||||
if (median > 0f) {
|
||||
dotDurationMs = dotDurationMs * 0.7f + median * 0.3f
|
||||
val wpm = 60.0f / (50.0f * dotDurationMs / 1000.0f)
|
||||
if (wpm in 5f..55f) speedWpm = wpm
|
||||
}
|
||||
}
|
||||
|
||||
fun getSpeed(): Float = speedWpm
|
||||
|
||||
fun reset() {
|
||||
dotDurationMs = 60f
|
||||
speedWpm = 20f
|
||||
recentDits.clear()
|
||||
currentSymbol.clear()
|
||||
textBuffer.clear()
|
||||
_decodedText = ""
|
||||
}
|
||||
|
||||
companion object {
|
||||
private val MORSE_TABLE = mapOf(
|
||||
"01" to 'A', "1000" to 'B', "1010" to 'C', "100" to 'D', "0" to 'E',
|
||||
"0010" to 'F', "110" to 'G', "0000" to 'H', "00" to 'I', "0111" to 'J',
|
||||
"101" to 'K', "0100" to 'L', "11" to 'M', "10" to 'N', "111" to 'O',
|
||||
"0110" to 'P', "1101" to 'Q', "010" to 'R', "000" to 'S', "1" to 'T',
|
||||
"001" to 'U', "0001" to 'V', "011" to 'W', "1001" to 'X', "1011" to 'Y',
|
||||
"1100" to 'Z', "01111" to '1', "00111" to '2', "00011" to '3',
|
||||
"00001" to '4', "00000" to '5', "10000" to '6', "11000" to '7',
|
||||
"11100" to '8', "11110" to '9', "11111" to '0',
|
||||
"010101" to '.', "110011" to ',', "001100" to '?', "011110" to '\'',
|
||||
"101011" to '!', "10010" to '/', "10110" to '(', "101101" to ')',
|
||||
"01000" to '&', "111000" to ':', "101010" to ';', "10001" to '=',
|
||||
"01010" to '+', "100001" to '-', "001101" to '_', "010010" to '"',
|
||||
"0001001" to '$', "011010" to '@'
|
||||
)
|
||||
fun morseToChar(morse: String): Char? = MORSE_TABLE[morse]
|
||||
}
|
||||
}
|
||||
|
||||
data class ToneResult(val symbol: Char?, val probability: Float)
|
||||
@@ -0,0 +1,114 @@
|
||||
/*
|
||||
* Look4Sat. Amateur radio satellite tracker and pass predictor.
|
||||
* Copyright (C) 2019-2026 Arty Bishop and contributors.
|
||||
*
|
||||
* This program is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
*/
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Multi-channel CW signal tracker.
|
||||
* Monitors the spectrogram for active frequency bins and extracts
|
||||
* energy envelopes for each detected signal.
|
||||
*
|
||||
* Inspired by CW Skimmer's multi-channel approach:
|
||||
* tracks all active signals in the passband simultaneously,
|
||||
* selects the best one for decoded output.
|
||||
*/
|
||||
internal class CwChannelTracker(
|
||||
private val spectrogram: CwSpectrogram,
|
||||
private val maxChannels: Int = 3
|
||||
) {
|
||||
data class Channel(
|
||||
val bin: Int,
|
||||
val frequency: Float,
|
||||
var active: Boolean = false,
|
||||
var energy: Float = 0f,
|
||||
val history: MutableList<Float> = mutableListOf(),
|
||||
var confidence: Float = 0f
|
||||
)
|
||||
|
||||
private val channels = Array(maxChannels) { Channel(0, 0f) }
|
||||
|
||||
/** Scan the current spectrogram column and update channel tracking. */
|
||||
fun update(): List<Channel> {
|
||||
val col = spectrogram.getCurrentColumn()
|
||||
val peaks = findPeaks(col, threshold = 0.3f, minDistance = 2)
|
||||
|
||||
// Update existing channels
|
||||
for (ch in channels) {
|
||||
if (ch.active) {
|
||||
if (peaks.contains(ch.bin)) {
|
||||
ch.energy = col[ch.bin]
|
||||
ch.history.add(ch.energy)
|
||||
if (ch.history.size > 40) ch.history.removeAt(0)
|
||||
ch.confidence = computeConfidence(ch.history)
|
||||
} else {
|
||||
// Signal lost — decay confidence
|
||||
ch.history.add(0f)
|
||||
if (ch.history.size > 40) ch.history.removeAt(0)
|
||||
ch.confidence *= 0.9f
|
||||
if (ch.confidence < 0.1f) ch.active = false
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Assign new peaks to inactive channels
|
||||
var peakIdx = 0
|
||||
for (ch in channels) {
|
||||
if (!ch.active && peakIdx < peaks.size) {
|
||||
val bin = peaks[peakIdx]
|
||||
val freq = spectrogram.binToFreq(bin)
|
||||
// Re-initialize channel
|
||||
channels[peakIdx] = Channel(bin, freq, true, col[bin], mutableListOf(), 0.5f)
|
||||
peakIdx++
|
||||
}
|
||||
}
|
||||
|
||||
return channels.filter { it.active }
|
||||
}
|
||||
|
||||
/** Find peak bins in the spectrum. */
|
||||
private fun findPeaks(spectrum: FloatArray, threshold: Float, minDistance: Int): List<Int> {
|
||||
val peaks = mutableListOf<Int>()
|
||||
for (i in 1 until spectrum.size - 1) {
|
||||
if (spectrum[i] > spectrum[i - 1] && spectrum[i] > spectrum[i + 1] && spectrum[i] > threshold) {
|
||||
if (peaks.isEmpty() || i - peaks.last() >= minDistance) {
|
||||
peaks.add(i)
|
||||
}
|
||||
}
|
||||
}
|
||||
return peaks.sortedByDescending { spectrum[it] }
|
||||
}
|
||||
|
||||
/** Compute confidence from energy history. Lower variance = higher confidence. */
|
||||
private fun computeConfidence(history: List<Float>): Float {
|
||||
if (history.size < 10) return 0.3f
|
||||
val recent = history.takeLast(10)
|
||||
val mean = recent.average().toFloat()
|
||||
val variance = recent.map { (it - mean) * (it - mean) }.average().toFloat()
|
||||
return if (mean > 0f) (mean / (mean + variance + 0.1f)).coerceIn(0f, 1f) else 0f
|
||||
}
|
||||
|
||||
/** Get the channel with highest confidence. */
|
||||
fun getBestChannel(): Channel? {
|
||||
return channels.filter { it.active }.maxByOrNull { it.confidence }
|
||||
}
|
||||
|
||||
fun reset() {
|
||||
for (i in channels.indices) {
|
||||
channels[i] = Channel(0, 0f)
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -19,52 +19,50 @@ package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
import kotlinx.coroutines.flow.MutableStateFlow
|
||||
import kotlinx.coroutines.flow.StateFlow
|
||||
import kotlin.math.abs
|
||||
|
||||
/**
|
||||
* CW (Morse code) decoder ported from ggerganov/ggmorse.
|
||||
* CW (Morse code) decoder v3 — Spectrogram-based multi-channel Bayesian decoder.
|
||||
*
|
||||
* Key improvements over v1:
|
||||
* - Automatic pitch detection (200-1200 Hz) via DFT
|
||||
* - Automatic speed detection (5-55 WPM) via interval clustering
|
||||
* - Adaptive threshold with signal statistics
|
||||
* - Resampling to 4 kHz base rate for efficiency
|
||||
* - Running Goertzel filter for tone detection
|
||||
* - First-order IIR bandpass filter (HP + LP)
|
||||
* Architecture inspired by Morse Expert / CW Skimmer (VE3NEA):
|
||||
* 1. FFT spectrogram creates a frequency×time matrix
|
||||
* 2. Multi-channel peak detector finds all active signals
|
||||
* 3. Per-channel energy envelope extraction
|
||||
* 4. Bayesian probability for symbol timing (Gaussian likelihood)
|
||||
* 5. Best channel selected for output
|
||||
*
|
||||
* Algorithm flow:
|
||||
* Audio buffer → Resample to 4 kHz → High-pass filter (200 Hz) →
|
||||
* Low-pass filter (1200 Hz) → Pitch detection (DFT, 200-1200 Hz) →
|
||||
* Running Goertzel at detected pitch → Adaptive threshold →
|
||||
* Signal interval timing → Speed estimation → Morse character lookup
|
||||
* Key advantages over v2 (ggmorse):
|
||||
* - Frequency-agnostic: monitors all 200-1200 Hz simultaneously
|
||||
* - Multi-channel: tracks up to 3 signals in parallel
|
||||
* - Bayesian: probability-based decisions, not hard thresholds
|
||||
* - Frequency drift tolerant: energy just moves between bins
|
||||
*/
|
||||
class CwDecoder(
|
||||
val sampleRate: Int = 8000,
|
||||
cwToneFreq: Float = -1f, // -1 = auto-detect
|
||||
minFreq: Float = 200f,
|
||||
maxFreq: Float = 1200f
|
||||
cwToneFreq: Float = -1f // ignored in v3 (auto-detect via spectrogram)
|
||||
) {
|
||||
companion object {
|
||||
private val MORSE_TABLE = mapOf(
|
||||
"01" to 'A', "1000" to 'B', "1010" to 'C', "100" to 'D', "0" to 'E',
|
||||
"0010" to 'F', "110" to 'G', "0000" to 'H', "00" to 'I', "0111" to 'J',
|
||||
"101" to 'K', "0100" to 'L', "11" to 'M', "10" to 'N', "111" to 'O',
|
||||
"0110" to 'P', "1101" to 'Q', "010" to 'R', "000" to 'S', "1" to 'T',
|
||||
"001" to 'U', "0001" to 'V', "011" to 'W', "1001" to 'X', "1011" to 'Y',
|
||||
"1100" to 'Z', "01111" to '1', "00111" to '2', "00011" to '3',
|
||||
"00001" to '4', "00000" to '5', "10000" to '6', "11000" to '7',
|
||||
"11100" to '8', "11110" to '9', "11111" to '0',
|
||||
"010101" to '.', "110011" to ',', "001100" to '?', "011110" to '\'',
|
||||
"101011" to '!', "10010" to '/', "10110" to '(', "101101" to ')',
|
||||
"01000" to '&', "111000" to ':', "101010" to ';', "10001" to '=',
|
||||
"01010" to '+', "100001" to '-', "001101" to '_', "010010" to '"',
|
||||
"0001001" to '$', "011010" to '@'
|
||||
)
|
||||
fun morseToChar(morse: String): Char? = MORSE_TABLE[morse]
|
||||
private val spectrogram = CwSpectrogram(
|
||||
fftSize = 256,
|
||||
hopSize = 64,
|
||||
sampleRate = sampleRate,
|
||||
minBin = 6,
|
||||
maxBin = 38,
|
||||
historyCols = 40
|
||||
)
|
||||
private val channelTracker = CwChannelTracker(spectrogram, maxChannels = 3)
|
||||
private val bayesianDecoder = CwBayesianDecoder()
|
||||
|
||||
private const val BASE_SAMPLE_RATE = 4000f
|
||||
private const val PITCH_DETECT_INTERVAL = 100 // frames between pitch scans
|
||||
}
|
||||
// Per-channel state
|
||||
private data class ChannelTiming(
|
||||
var isSignal: Boolean = false,
|
||||
var toneSamples: Int = 0,
|
||||
var gapSamples: Int = 0
|
||||
)
|
||||
private val timingStates = Array(3) { ChannelTiming() }
|
||||
|
||||
// Sample period in milliseconds (at 4 kHz effective rate for timing)
|
||||
// The spectrogram processes at native sample rate, but timing analysis
|
||||
// uses the spectrogram column rate: hopSize/sampleRate seconds per column
|
||||
private val samplePeriodMs = 1000f * hopSize / sampleRate
|
||||
|
||||
// Output flows
|
||||
private val _decodedTextFlow = MutableStateFlow("")
|
||||
@@ -79,223 +77,85 @@ class CwDecoder(
|
||||
private val _estimatedSpeed = MutableStateFlow<Float?>(null)
|
||||
val estimatedSpeed: StateFlow<Float?> = _estimatedSpeed
|
||||
|
||||
// DSP components
|
||||
private val resampler = CwResampler(sampleRate.toFloat(), BASE_SAMPLE_RATE)
|
||||
private val hpFilter = CwFilter()
|
||||
private val lpFilter = CwFilter()
|
||||
private val pitchDetector = CwPitchDetector(BASE_SAMPLE_RATE, minFreq, maxFreq)
|
||||
private val goertzel = CwGoertzel()
|
||||
|
||||
// Decoder state
|
||||
private var decodedText = StringBuilder()
|
||||
private var currentLetter = StringBuilder()
|
||||
private var isSignal = false
|
||||
private var signalOnSamples = 0
|
||||
private var signalOffSamples = 0
|
||||
private var pitchEstimate = if (cwToneFreq > 0f) cwToneFreq else -1f
|
||||
private var pitchConfidenceCounter = 0
|
||||
private var noiseFloor = 0.0f
|
||||
private var signalPeak = 0.0f
|
||||
private var speedEstimate = 20f // initial guess: 20 WPM
|
||||
private var isPitchLocked = cwToneFreq > 0f
|
||||
// Frame counter for periodic updates
|
||||
private var frameCount = 0
|
||||
|
||||
init {
|
||||
if (isPitchLocked) {
|
||||
if (cwToneFreq > 0f) {
|
||||
_estimatedPitch.value = cwToneFreq
|
||||
}
|
||||
}
|
||||
|
||||
// Interval history for speed estimation
|
||||
private val intervalHistory = mutableListOf<Int>() // lengths of dits (type 0 only)
|
||||
|
||||
// Keep track of last processed sample for the goertzel filter
|
||||
private var goertzelSampleCount = 0
|
||||
companion object {
|
||||
private const val hopSize = 64
|
||||
}
|
||||
|
||||
fun processBuffer(buffer: FloatArray) {
|
||||
// 1. Resample to 4 kHz base rate
|
||||
val resampled = resampler.process(buffer)
|
||||
// 1. Feed samples to spectrogram (generates FFT waterfall)
|
||||
spectrogram.addSamples(buffer)
|
||||
|
||||
for (sample in resampled) {
|
||||
// 2. Bandpass filter chain: 200 Hz HP → 1200 Hz LP
|
||||
val hp = hpFilter.highPass(sample, 200f, BASE_SAMPLE_RATE)
|
||||
val filtered = lpFilter.lowPass(hp, 1200f, BASE_SAMPLE_RATE)
|
||||
val absVal = abs(filtered)
|
||||
// 2. Update channel tracker (find active frequency bins)
|
||||
val activeChannels = channelTracker.update()
|
||||
|
||||
// 3. Update noise floor and signal peak (running statistics)
|
||||
noiseFloor = 0.999f * noiseFloor + 0.001f * absVal
|
||||
if (absVal > signalPeak) {
|
||||
signalPeak = absVal
|
||||
} else {
|
||||
signalPeak = 0.999f * signalPeak
|
||||
}
|
||||
// 3. For each active channel, extract timing
|
||||
for ((idx, channel) in activeChannels.withIndex()) {
|
||||
if (idx >= timingStates.size) break
|
||||
val state = timingStates[idx]
|
||||
val col = spectrogram.getCurrentColumn()
|
||||
val energy = if (channel.bin in col.indices) col[channel.bin] else 0f
|
||||
|
||||
// 4. Adaptive threshold
|
||||
val threshold = (noiseFloor + (signalPeak - noiseFloor) * 0.3f)
|
||||
_signalStrength.value = if (signalPeak > 0f && threshold > 0f) {
|
||||
((signalPeak - threshold) / signalPeak).coerceIn(0f, 1f)
|
||||
} else {
|
||||
0f
|
||||
}
|
||||
// Adaptive threshold: 30% above noise floor
|
||||
val threshold = 0.3f + (energy - 0.3f) * 0.3f
|
||||
|
||||
// 5. Run Goertzel filter if pitch is locked
|
||||
if (isPitchLocked && pitchEstimate > 0f) {
|
||||
goertzel.process(filtered)
|
||||
goertzelSampleCount++
|
||||
}
|
||||
|
||||
// 6. Signal detection with adaptive threshold
|
||||
if (absVal > threshold) {
|
||||
if (!isSignal) {
|
||||
// Rising edge — process the silence gap that just ended
|
||||
if (signalOffSamples > 0) {
|
||||
processGap(signalOffSamples)
|
||||
if (energy > threshold) {
|
||||
if (!state.isSignal) {
|
||||
// Rising edge — process gap
|
||||
if (state.gapSamples > 0) {
|
||||
val gapMs = state.gapSamples * samplePeriodMs
|
||||
bayesianDecoder.processGap(gapMs)
|
||||
}
|
||||
signalOffSamples = 0
|
||||
isSignal = true
|
||||
state.gapSamples = 0
|
||||
state.isSignal = true
|
||||
}
|
||||
signalOnSamples++
|
||||
state.toneSamples++
|
||||
} else {
|
||||
if (isSignal) {
|
||||
// Falling edge — process the tone that just ended
|
||||
processTone(signalOnSamples)
|
||||
signalOnSamples = 0
|
||||
isSignal = false
|
||||
if (state.isSignal) {
|
||||
// Falling edge — process tone
|
||||
val toneMs = state.toneSamples * samplePeriodMs
|
||||
bayesianDecoder.processTone(toneMs)
|
||||
state.toneSamples = 0
|
||||
state.isSignal = false
|
||||
}
|
||||
signalOffSamples++
|
||||
state.gapSamples++
|
||||
}
|
||||
}
|
||||
|
||||
// 7. Periodic pitch detection (every ~100 frames)
|
||||
if (!isPitchLocked) {
|
||||
pitchConfidenceCounter++
|
||||
if (pitchConfidenceCounter >= PITCH_DETECT_INTERVAL) {
|
||||
pitchConfidenceCounter = 0
|
||||
val pitch = pitchDetector.findPitch(resampled)
|
||||
if (pitch != null) {
|
||||
pitchEstimate = pitch
|
||||
_estimatedPitch.value = pitch
|
||||
isPitchLocked = true
|
||||
goertzel.init(BASE_SAMPLE_RATE, pitch)
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Continuous pitch tracking: re-check periodically to handle Doppler drift
|
||||
pitchConfidenceCounter++
|
||||
if (pitchConfidenceCounter >= PITCH_DETECT_INTERVAL * 5) {
|
||||
pitchConfidenceCounter = 0
|
||||
// Narrow scan: ±100 Hz around current pitch estimate
|
||||
val narrowDetector = CwPitchDetector(
|
||||
BASE_SAMPLE_RATE,
|
||||
(pitchEstimate - 100f).coerceAtLeast(200f),
|
||||
(pitchEstimate + 100f).coerceAtMost(1200f),
|
||||
5f
|
||||
)
|
||||
val pitch = narrowDetector.findPitch(resampled)
|
||||
if (pitch != null && kotlin.math.abs(pitch - pitchEstimate) > 20f) {
|
||||
pitchEstimate = pitch
|
||||
_estimatedPitch.value = pitch
|
||||
goertzel.init(BASE_SAMPLE_RATE, pitch)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Push latest decoded text
|
||||
_decodedTextFlow.value = decodedText.toString()
|
||||
}
|
||||
|
||||
private fun processTone(samples: Int) {
|
||||
val dotDuration = samplesForDot()
|
||||
if (dotDuration <= 0) return
|
||||
|
||||
val ratio = samples.toFloat() / dotDuration
|
||||
|
||||
if (ratio < 1.5f) {
|
||||
currentLetter.append('0') // 0 = dot
|
||||
// Track dit lengths for speed estimation
|
||||
intervalHistory.add(samples)
|
||||
if (intervalHistory.size > 20) intervalHistory.removeAt(0)
|
||||
} else if (ratio < 5.0f) {
|
||||
currentLetter.append('1') // 1 = dash
|
||||
}
|
||||
// else: ignore very long tones (likely noise/interference)
|
||||
|
||||
// Update speed estimate from recent dits
|
||||
updateSpeedEstimate()
|
||||
}
|
||||
|
||||
private fun processGap(samples: Int) {
|
||||
val dotDuration = samplesForDot()
|
||||
if (dotDuration <= 0) return
|
||||
|
||||
val gapRatio = samples.toFloat() / dotDuration
|
||||
|
||||
if (currentLetter.isNotEmpty()) {
|
||||
// Inter-character gap (3+ dot durations)
|
||||
if (gapRatio >= 2.5f) {
|
||||
val char = morseToChar(currentLetter.toString())
|
||||
if (char != null) {
|
||||
decodedText.append(char)
|
||||
}
|
||||
currentLetter.clear()
|
||||
|
||||
// Word gap (7+ dot durations)
|
||||
if (gapRatio >= 7f) {
|
||||
decodedText.append(' ')
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Word gap (7+ dot durations, no letter in progress)
|
||||
if (gapRatio >= 7f) {
|
||||
decodedText.append(' ')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private fun samplesForDot(): Int {
|
||||
// Convert WPM to samples at 4 kHz base rate
|
||||
// Using standard formula: dot = 60/(50*WPM) seconds
|
||||
return ((BASE_SAMPLE_RATE * 60.0 / (50.0 * speedEstimate)).toInt()).coerceAtLeast(1)
|
||||
}
|
||||
|
||||
private fun updateSpeedEstimate() {
|
||||
if (intervalHistory.size < 3) return
|
||||
|
||||
// Use median of recent dit lengths for speed estimation
|
||||
val sorted = intervalHistory.sorted()
|
||||
val median = sorted[sorted.size / 2].toFloat()
|
||||
|
||||
if (median > 0f) {
|
||||
val newSpeed = 60.0f / (50.0f * median / BASE_SAMPLE_RATE)
|
||||
if (newSpeed in 5f..55f) {
|
||||
// Smooth speed update (70% old, 30% new)
|
||||
speedEstimate = speedEstimate * 0.7f + newSpeed * 0.3f
|
||||
_estimatedSpeed.value = speedEstimate
|
||||
// 4. Update outputs from best channel
|
||||
frameCount++
|
||||
if (frameCount % 5 == 0) { // Every 5 frames
|
||||
val bestChannel = channelTracker.getBestChannel()
|
||||
if (bestChannel != null) {
|
||||
_estimatedPitch.value = bestChannel.frequency
|
||||
_signalStrength.value = bestChannel.confidence
|
||||
_estimatedSpeed.value = bayesianDecoder.getSpeed()
|
||||
}
|
||||
_decodedTextFlow.value = bayesianDecoder.decodedText
|
||||
}
|
||||
}
|
||||
|
||||
fun resetDecoder() {
|
||||
isSignal = false
|
||||
signalOnSamples = 0
|
||||
signalOffSamples = 0
|
||||
decodedText.clear()
|
||||
currentLetter.clear()
|
||||
intervalHistory.clear()
|
||||
noiseFloor = 0.0f
|
||||
signalPeak = 0.0f
|
||||
speedEstimate = 20f
|
||||
if (!isPitchLocked) {
|
||||
pitchEstimate = -1f
|
||||
pitchConfidenceCounter = 0
|
||||
spectrogram.reset()
|
||||
channelTracker.reset()
|
||||
bayesianDecoder.reset()
|
||||
for (state in timingStates) {
|
||||
state.isSignal = false
|
||||
state.toneSamples = 0
|
||||
state.gapSamples = 0
|
||||
}
|
||||
goertzelSampleCount = 0
|
||||
hpFilter.reset()
|
||||
lpFilter.reset()
|
||||
resampler.reset()
|
||||
goertzel.reset()
|
||||
frameCount = 0
|
||||
_decodedTextFlow.value = ""
|
||||
_signalStrength.value = 0f
|
||||
_estimatedPitch.value = if (isPitchLocked) pitchEstimate else null
|
||||
_estimatedPitch.value = null
|
||||
_estimatedSpeed.value = null
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,87 @@
|
||||
/*
|
||||
* Look4Sat. Amateur radio satellite tracker and pass predictor.
|
||||
* Copyright (C) 2019-2026 Arty Bishop and contributors.
|
||||
*
|
||||
* This program is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
*/
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
import kotlin.math.cos
|
||||
import kotlin.math.sqrt
|
||||
|
||||
/**
|
||||
* Radix-2 FFT for real-valued input.
|
||||
* Produces magnitude spectrum for the first N/2+1 bins.
|
||||
* Used by CwSpectrogram for time-frequency analysis.
|
||||
*/
|
||||
internal class CwFFT(private val n: Int) {
|
||||
init {
|
||||
require(n > 0 && n and (n - 1) == 0) { "FFT size must be power of 2, got $n" }
|
||||
}
|
||||
|
||||
private val cosTable = FloatArray(n / 2)
|
||||
private val sinTable = FloatArray(n / 2)
|
||||
|
||||
init {
|
||||
for (i in 0 until n / 2) {
|
||||
val angle = -2.0 * kotlin.math.PI * i / n
|
||||
cosTable[i] = cos(angle).toFloat()
|
||||
sinTable[i] = kotlin.math.sin(angle).toFloat()
|
||||
}
|
||||
}
|
||||
|
||||
/** Compute magnitude spectrum for real input. Returns array of size n/2+1. */
|
||||
fun magnitudeSpectrum(input: FloatArray): FloatArray {
|
||||
require(input.size == n) { "Input size must be $n, got ${input.size}" }
|
||||
|
||||
val real = input.copyOf()
|
||||
val imag = FloatArray(n)
|
||||
|
||||
// Bit-reversal permutation
|
||||
var j = 0
|
||||
for (i in 1 until n) {
|
||||
var bit = n shr 1
|
||||
while (j and bit != 0) { j = j xor bit; bit = bit shr 1 }
|
||||
j = j xor bit
|
||||
if (i < j) {
|
||||
var tmp = real[i]; real[i] = real[j]; real[j] = tmp
|
||||
}
|
||||
}
|
||||
|
||||
// Radix-2 Cooley-Tukey FFT
|
||||
var len = 2
|
||||
while (len <= n) {
|
||||
val half = len / 2
|
||||
val step = n / len
|
||||
for (i in 0 until n step len) {
|
||||
for (k in 0 until half) {
|
||||
val tReal = real[i + k + half] * cosTable[k * step] - imag[i + k + half] * sinTable[k * step]
|
||||
val tImag = real[i + k + half] * sinTable[k * step] + imag[i + k + half] * cosTable[k * step]
|
||||
real[i + k + half] = real[i + k] - tReal
|
||||
imag[i + k + half] = imag[i + k] - tImag
|
||||
real[i + k] += tReal
|
||||
imag[i + k] += tImag
|
||||
}
|
||||
}
|
||||
len = len shl 1
|
||||
}
|
||||
|
||||
// Magnitude spectrum (first N/2+1 bins)
|
||||
val mag = FloatArray(n / 2 + 1)
|
||||
for (i in 0..n / 2) {
|
||||
mag[i] = sqrt(real[i] * real[i] + imag[i] * imag[i]) / n
|
||||
}
|
||||
return mag
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,152 @@
|
||||
/*
|
||||
* Look4Sat. Amateur radio satellite tracker and pass predictor.
|
||||
* Copyright (C) 2019-2026 Arty Bishop and contributors.
|
||||
*
|
||||
* This program is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
*/
|
||||
package com.rtbishop.look4sat.core.domain.cw
|
||||
|
||||
/**
|
||||
* Sliding-window spectrogram for CW decoding.
|
||||
* Maintains a time-frequency matrix updated with each audio frame.
|
||||
*
|
||||
* FFT size: 256, hop size: 64, sample rate: 4000 (or native)
|
||||
* Frequency bins: 6..38 (187-1187 Hz, covers typical CW range)
|
||||
* History: 40 columns (320 ms window)
|
||||
* Time resolution: 64/4000 = 16 ms, Frequency resolution: 4000/256 = 15.625 Hz
|
||||
*/
|
||||
internal class CwSpectrogram(
|
||||
private val fftSize: Int = 256,
|
||||
private val hopSize: Int = 64,
|
||||
private val sampleRate: Int = 4000,
|
||||
private val minBin: Int = 6,
|
||||
private val maxBin: Int = 38,
|
||||
private val historyCols: Int = 40
|
||||
) {
|
||||
private val fft = CwFFT(fftSize)
|
||||
val numBins: Int get() = maxBin - minBin + 1
|
||||
|
||||
// Hanning window
|
||||
private val hanning = FloatArray(fftSize) {
|
||||
(0.5 - 0.5 * kotlin.math.cos(2.0 * kotlin.math.PI * it / (fftSize - 1))).toFloat()
|
||||
}
|
||||
|
||||
// Spectrogram data: [timeCol][freqBin]
|
||||
private val spectrogram = Array(historyCols) { FloatArray(numBins) }
|
||||
private var currentCol = 0
|
||||
private var samplesBuffered = 0
|
||||
private val buffer = FloatArray(fftSize)
|
||||
|
||||
// Per-bin running energy for normalization
|
||||
private val binEnergy = FloatArray(numBins) { 1f }
|
||||
private val alpha = 0.95f
|
||||
|
||||
/** Add audio samples, compute FFTs for each complete hop. */
|
||||
fun addSamples(samples: FloatArray) {
|
||||
var offset = 0
|
||||
while (offset < samples.size) {
|
||||
val needed = fftSize - samplesBuffered
|
||||
val copyLen = minOf(needed, samples.size - offset)
|
||||
System.arraycopy(samples, offset, buffer, samplesBuffered, copyLen)
|
||||
samplesBuffered += copyLen
|
||||
offset += copyLen
|
||||
|
||||
if (samplesBuffered >= fftSize) {
|
||||
processFrame()
|
||||
// Shift buffer: keep last (fftSize - hopSize) samples
|
||||
System.arraycopy(buffer, hopSize, buffer, 0, fftSize - hopSize)
|
||||
samplesBuffered = fftSize - hopSize
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private fun processFrame() {
|
||||
// Apply Hanning window
|
||||
val windowed = FloatArray(fftSize) { buffer[it] * hanning[it] }
|
||||
|
||||
// Compute FFT magnitude spectrum
|
||||
val mag = fft.magnitudeSpectrum(windowed)
|
||||
|
||||
// Update spectrogram column
|
||||
val col = spectrogram[currentCol]
|
||||
for (b in 0 until numBins) {
|
||||
val binIdx = minBin + b
|
||||
val rawMag = mag[binIdx]
|
||||
// Running energy normalization
|
||||
binEnergy[b] = alpha * binEnergy[b] + (1 - alpha) * rawMag
|
||||
col[b] = if (binEnergy[b] > 1e-6f) rawMag / binEnergy[b] else 0f
|
||||
}
|
||||
|
||||
currentCol = (currentCol + 1) % historyCols
|
||||
}
|
||||
|
||||
/** Get the current spectrogram as a 2D array in chronological order. */
|
||||
fun getSpectrogram(): Array<FloatArray> {
|
||||
val result = Array(historyCols) { i ->
|
||||
val srcIdx = (currentCol + i) % historyCols
|
||||
spectrogram[srcIdx].copyOf()
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
/** Get the most recent column (current energy across all frequencies). */
|
||||
fun getCurrentColumn(): FloatArray {
|
||||
val prevCol = (currentCol - 1 + historyCols) % historyCols
|
||||
return spectrogram[prevCol].copyOf()
|
||||
}
|
||||
|
||||
/** Find the frequency bin with peak energy. Returns -1 if no significant signal. */
|
||||
fun findPeakBin(): Int {
|
||||
val col = getCurrentColumn()
|
||||
var maxBin = -1
|
||||
var maxVal = 0f
|
||||
for (i in col.indices) {
|
||||
if (col[i] > maxVal) {
|
||||
maxVal = col[i]
|
||||
maxBin = i
|
||||
}
|
||||
}
|
||||
return if (maxVal > 0.3f) maxBin else -1
|
||||
}
|
||||
|
||||
/** Get energy at a specific bin over the last N columns in chronological order. */
|
||||
fun getBinEnergy(bin: Int, numCols: Int): FloatArray {
|
||||
val clamped = minOf(numCols, historyCols)
|
||||
val result = FloatArray(clamped)
|
||||
for (i in 0 until clamped) {
|
||||
val colIdx = (currentCol - clamped + i + historyCols) % historyCols
|
||||
result[i] = spectrogram[colIdx][bin]
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
/** Get the bin index for a frequency in Hz. */
|
||||
fun freqToBin(freqHz: Float): Int {
|
||||
val bin = (freqHz * fftSize / sampleRate).toInt()
|
||||
return (bin - minBin).coerceIn(0, numBins - 1)
|
||||
}
|
||||
|
||||
/** Get the center frequency for a bin. */
|
||||
fun binToFreq(bin: Int): Float {
|
||||
return (minBin + bin).toFloat() * sampleRate / fftSize
|
||||
}
|
||||
|
||||
fun reset() {
|
||||
for (col in spectrogram) col.fill(0f)
|
||||
currentCol = 0
|
||||
samplesBuffered = 0
|
||||
buffer.fill(0f)
|
||||
binEnergy.fill(1f)
|
||||
}
|
||||
}
|
||||
@@ -28,240 +28,274 @@ class CwDecoderTest {
|
||||
|
||||
@Test
|
||||
fun morseToChar_basicLetters() {
|
||||
assertEquals('A', CwDecoder.morseToChar("01"))
|
||||
assertEquals('B', CwDecoder.morseToChar("1000"))
|
||||
assertEquals('S', CwDecoder.morseToChar("000"))
|
||||
assertEquals('O', CwDecoder.morseToChar("111"))
|
||||
assertEquals('A', CwBayesianDecoder.morseToChar("01"))
|
||||
assertEquals('S', CwBayesianDecoder.morseToChar("000"))
|
||||
assertEquals('O', CwBayesianDecoder.morseToChar("111"))
|
||||
}
|
||||
|
||||
@Test
|
||||
fun morseToChar_numbers() {
|
||||
assertEquals('1', CwDecoder.morseToChar("01111"))
|
||||
assertEquals('5', CwDecoder.morseToChar("00000"))
|
||||
assertEquals('0', CwDecoder.morseToChar("11111"))
|
||||
assertEquals('1', CwBayesianDecoder.morseToChar("01111"))
|
||||
assertEquals('0', CwBayesianDecoder.morseToChar("11111"))
|
||||
}
|
||||
|
||||
@Test
|
||||
fun morseToChar_unknown_returnsNull() {
|
||||
assertNull(CwDecoder.morseToChar("......."))
|
||||
assertNull(CwDecoder.morseToChar(""))
|
||||
assertNull(CwDecoder.morseToChar("01-01"))
|
||||
assertNull(CwBayesianDecoder.morseToChar("......."))
|
||||
assertNull(CwBayesianDecoder.morseToChar(""))
|
||||
}
|
||||
|
||||
// --- Resampler ---
|
||||
// --- FFT ---
|
||||
|
||||
@Test
|
||||
fun resampler_downsampleReducesSize() {
|
||||
val resampler = CwResampler(8000f, 4000f)
|
||||
val input = FloatArray(8000) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() }
|
||||
val output = resampler.process(input)
|
||||
assertTrue("Output size ${output.size} should be ~4000", output.size in 3800..4200)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun resampler_emptyInput_returnsEmpty() {
|
||||
val resampler = CwResampler(8000f, 4000f)
|
||||
val output = resampler.process(FloatArray(0))
|
||||
assertTrue(output.isEmpty())
|
||||
}
|
||||
|
||||
@Test
|
||||
fun resampler_sameRate_returnsSameSize() {
|
||||
val resampler = CwResampler(4000f, 4000f)
|
||||
val input = FloatArray(100) { it.toFloat() }
|
||||
val output = resampler.process(input)
|
||||
assertTrue("Output size should be ~100", output.size in 95..105)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun resampler_resetClearsState() {
|
||||
val resampler = CwResampler(8000f, 4000f)
|
||||
val input = FloatArray(100) { 1f }
|
||||
resampler.process(input)
|
||||
resampler.reset()
|
||||
// Should not crash
|
||||
resampler.process(FloatArray(100) { 0f })
|
||||
}
|
||||
|
||||
// --- Filter ---
|
||||
|
||||
@Test
|
||||
fun filter_highPass_doesNotCrash() {
|
||||
val filter = CwFilter()
|
||||
val sampleRate = 4000f
|
||||
// Test that the filter runs without crashing and produces finite values
|
||||
val output = FloatArray(100) { filter.highPass(1.0f, 200f, sampleRate) }
|
||||
output.forEach { assertFalse("Output should be finite: $it", it.isNaN() || it.isInfinite()) }
|
||||
}
|
||||
|
||||
@Test
|
||||
fun filter_lowPass_smoothsSignal() {
|
||||
val filter = CwFilter()
|
||||
val sampleRate = 4000f
|
||||
// High frequency noise
|
||||
val output = FloatArray(100) { filter.lowPass((sin(2.0 * PI * 1000.0 * it / sampleRate)).toFloat(), 500f, sampleRate) }
|
||||
val maxVal = output.maxOrNull() ?: 1f
|
||||
assertTrue("High freq should be attenuated, max=$maxVal", maxVal < 0.8f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun filter_reset() {
|
||||
val filter = CwFilter()
|
||||
filter.highPass(1f, 200f, 4000f)
|
||||
filter.reset()
|
||||
// Should not crash
|
||||
assertEquals(0f, filter.highPass(0f, 200f, 4000f), 0.001f)
|
||||
}
|
||||
|
||||
// --- Goertzel ---
|
||||
|
||||
@Test
|
||||
fun goertzel_detectsPresentTone() {
|
||||
val sampleRate = 4000f
|
||||
val targetFreq = 700f
|
||||
val goertzel = CwGoertzel()
|
||||
goertzel.init(sampleRate, targetFreq)
|
||||
// Generate 700 Hz tone
|
||||
for (i in 0 until sampleRate.toInt()) {
|
||||
goertzel.process((sin(2.0 * PI * targetFreq * i / sampleRate)).toFloat())
|
||||
fun fft_magnitudeSpectrum_detectsTone() {
|
||||
val fft = CwFFT(256)
|
||||
val sampleRate = 8000f
|
||||
val freq = 700f
|
||||
val buffer = FloatArray(256) { (sin(2.0 * PI * freq * it / sampleRate)).toFloat() }
|
||||
val mag = fft.magnitudeSpectrum(buffer)
|
||||
// Peak should be at bin around 700 * 256 / 8000 ≈ 22.4
|
||||
var maxBin = 0
|
||||
var maxVal = 0f
|
||||
for (i in mag.indices) {
|
||||
if (mag[i] > maxVal) { maxVal = mag[i]; maxBin = i }
|
||||
}
|
||||
val power = goertzel.getPower()
|
||||
assertTrue("Goertzel should detect present tone, got $power", power > 0.1f)
|
||||
assertTrue("Peak bin $maxBin should be near 22", maxBin in 18..26)
|
||||
assertTrue("Peak value $maxVal should be positive", maxVal > 0.01f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun goertzel_rejectsAbsentTone() {
|
||||
val sampleRate = 4000f
|
||||
val targetFreq = 700f
|
||||
val goertzel = CwGoertzel()
|
||||
goertzel.init(sampleRate, targetFreq)
|
||||
// Generate 2000 Hz tone (no match)
|
||||
for (i in 0 until sampleRate.toInt()) {
|
||||
goertzel.process((sin(2.0 * PI * 2000f * i / sampleRate)).toFloat())
|
||||
fun fft_magnitudeSpectrum_silence_isFlat() {
|
||||
val fft = CwFFT(256)
|
||||
val buffer = FloatArray(256) { 0f }
|
||||
val mag = fft.magnitudeSpectrum(buffer)
|
||||
for (v in mag) assertEquals("Silence spectrum should be 0, got $v", 0f, v, 1e-6f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun fft_rejectsWrongSize() {
|
||||
assertThrows(IllegalArgumentException::class.java) { CwFFT(100) }
|
||||
}
|
||||
|
||||
// --- Spectrogram ---
|
||||
|
||||
@Test
|
||||
fun spectrogram_addSamples_updatesEnergy() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
val freq = 700f
|
||||
// Feed multiple frames to stabilize energy normalization
|
||||
for (i in 0..5) {
|
||||
val buffer = FloatArray(256) { (sin(2.0 * PI * freq * it / 8000.0)).toFloat() }
|
||||
spec.addSamples(buffer)
|
||||
}
|
||||
val power = goertzel.getPower()
|
||||
assertTrue("Goertzel should reject absent tone, got $power", power < 0.1f)
|
||||
val col = spec.getCurrentColumn()
|
||||
val peakBin = spec.findPeakBin()
|
||||
assertTrue("Peak bin $peakBin should be >= 0", peakBin >= 0)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun goertzel_reset() {
|
||||
val goertzel = CwGoertzel()
|
||||
goertzel.init(4000f, 700f)
|
||||
goertzel.process(1f)
|
||||
goertzel.reset()
|
||||
assertEquals(0f, goertzel.getPower(), 0.001f)
|
||||
}
|
||||
|
||||
// --- Pitch detector ---
|
||||
|
||||
@Test
|
||||
fun pitchDetector_findsCorrectFrequency() {
|
||||
val sampleRate = 4000f
|
||||
val detector = CwPitchDetector(sampleRate, 200f, 1200f, 10f)
|
||||
val targetFreq = 700f
|
||||
val buffer = FloatArray(sampleRate.toInt()) { (sin(2.0 * PI * targetFreq * it / sampleRate)).toFloat() }
|
||||
val pitch = detector.findPitch(buffer)
|
||||
assertNotNull("Pitch should be detected", pitch)
|
||||
if (pitch != null) {
|
||||
assertTrue("Detected pitch $pitch should be close to 700 Hz", pitch in 680f..720f)
|
||||
fun spectrogram_findPeakBin_returnsValidBin() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
// Add multiple frames of 700 Hz tone
|
||||
for (i in 0..5) {
|
||||
val buffer = FloatArray(256) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() }
|
||||
spec.addSamples(buffer)
|
||||
}
|
||||
val peakBin = spec.findPeakBin()
|
||||
assertTrue("Peak bin should be >= 0, got $peakBin", peakBin >= 0)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun pitchDetector_findsDifferentFrequency() {
|
||||
val sampleRate = 4000f
|
||||
val detector = CwPitchDetector(sampleRate, 200f, 1200f, 10f)
|
||||
val targetFreq = 500f
|
||||
val buffer = FloatArray(sampleRate.toInt()) { (sin(2.0 * PI * targetFreq * it / sampleRate)).toFloat() }
|
||||
val pitch = detector.findPitch(buffer)
|
||||
assertNotNull("Pitch should be detected", pitch)
|
||||
if (pitch != null) {
|
||||
assertTrue("Detected pitch $pitch should be close to 500 Hz", pitch in 480f..520f)
|
||||
fun spectrogram_freqToBin_roundtrip() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
val freq = 700f
|
||||
val bin = spec.freqToBin(freq)
|
||||
val backFreq = spec.binToFreq(bin)
|
||||
assertTrue("Freq $freq → bin $bin → freq $backFreq", backFreq > 600f && backFreq < 800f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun spectrogram_getBinEnergy_returnsCorrectLength() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
val energy = spec.getBinEnergy(0, 10)
|
||||
assertEquals(10, energy.size)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun spectrogram_reset() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
spec.addSamples(FloatArray(256) { 1f })
|
||||
spec.reset()
|
||||
assertEquals(-1, spec.findPeakBin())
|
||||
}
|
||||
|
||||
// --- Bayesian decoder ---
|
||||
|
||||
@Test
|
||||
fun bayesian_processTone_dit() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
// At 20 WPM, dot = 60 ms
|
||||
val result = decoder.processTone(60f)
|
||||
assertEquals('0', result.symbol)
|
||||
assertTrue("Dit probability should be positive", result.probability > 0.1f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_processTone_dash() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
// Dash = 3 * dot = 180 ms
|
||||
val result = decoder.processTone(180f)
|
||||
assertEquals('1', result.symbol)
|
||||
assertTrue("Dash probability should be positive", result.probability > 0.1f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_processTone_unknown_returnsNull() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
// Very long tone — low probability for both dit and dash
|
||||
val result = decoder.processTone(5000f)
|
||||
assertNull(result.symbol)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_processGap_interChar_returnsChar() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
decoder.processTone(60f) // dit
|
||||
decoder.processTone(60f) // dit
|
||||
decoder.processTone(60f) // dit
|
||||
// 3 dots = "000" = 'S'
|
||||
val char = decoder.processGap(180f) // 3 * dot = inter-char gap
|
||||
assertEquals('S', char)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_processGap_wordGap_addsSpace() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
decoder.processTone(60f) // dit = 'E'
|
||||
decoder.processGap(180f) // inter-char gap
|
||||
// Now word gap
|
||||
val space = decoder.processGap(420f) // 7 * dot
|
||||
assertEquals(' ', space)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_decodedText_accumulates() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
decoder.processTone(60f) // dit = 'E'
|
||||
decoder.processGap(180f) // inter-char
|
||||
assertTrue(decoder.decodedText.isNotEmpty())
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_reset() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
decoder.processTone(60f)
|
||||
decoder.reset()
|
||||
assertEquals("", decoder.decodedText)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun bayesian_getSpeed() {
|
||||
val decoder = CwBayesianDecoder()
|
||||
// Send 3 dits at 20 WPM (60 ms each)
|
||||
decoder.processTone(60f)
|
||||
decoder.processTone(60f)
|
||||
decoder.processTone(60f)
|
||||
val speed = decoder.getSpeed()
|
||||
assertTrue("Speed should be ~20 WPM, got $speed", speed > 15f && speed < 30f)
|
||||
}
|
||||
|
||||
// --- Channel tracker ---
|
||||
|
||||
@Test
|
||||
fun channelTracker_initialState() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
val tracker = CwChannelTracker(spec)
|
||||
val channels = tracker.update()
|
||||
assertTrue("No channels should be active initially", channels.isEmpty())
|
||||
}
|
||||
|
||||
@Test
|
||||
fun channelTracker_detectsTone() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
// Feed a tone
|
||||
for (i in 0..5) {
|
||||
spec.addSamples(FloatArray(256) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() })
|
||||
}
|
||||
val tracker = CwChannelTracker(spec)
|
||||
val channels = tracker.update()
|
||||
assertTrue("Should detect at least 1 channel", channels.isNotEmpty())
|
||||
}
|
||||
|
||||
@Test
|
||||
fun pitchDetector_returnsNullForSilence() {
|
||||
val detector = CwPitchDetector(4000f)
|
||||
val buffer = FloatArray(4000) { 0f }
|
||||
val pitch = detector.findPitch(buffer)
|
||||
assertNull("Pitch should be null for silence", pitch)
|
||||
fun channelTracker_bestChannel() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
for (i in 0..5) {
|
||||
spec.addSamples(FloatArray(256) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() })
|
||||
}
|
||||
val tracker = CwChannelTracker(spec)
|
||||
tracker.update()
|
||||
val best = tracker.getBestChannel()
|
||||
assertNotNull("Best channel should exist", best)
|
||||
if (best != null) assertTrue(best.frequency in 600f..800f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun pitchDetector_emptyBuffer() {
|
||||
val detector = CwPitchDetector(4000f)
|
||||
assertNull(detector.findPitch(FloatArray(0)))
|
||||
fun channelTracker_reset() {
|
||||
val spec = CwSpectrogram(sampleRate = 8000)
|
||||
for (i in 0..5) {
|
||||
spec.addSamples(FloatArray(256) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() })
|
||||
}
|
||||
val tracker = CwChannelTracker(spec)
|
||||
tracker.update()
|
||||
tracker.reset()
|
||||
assertNull(tracker.getBestChannel())
|
||||
}
|
||||
|
||||
// --- Decoder state ---
|
||||
// --- Full decoder ---
|
||||
|
||||
@Test
|
||||
fun cwDecoder_initialState() {
|
||||
fun decoder_initialState() {
|
||||
val decoder = CwDecoder()
|
||||
assertEquals("", decoder.decodedTextFlow.value)
|
||||
assertEquals(0f, decoder.signalStrength.value, 0.001f)
|
||||
assertNull(decoder.estimatedPitch.value)
|
||||
assertNull(decoder.estimatedSpeed.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun cwDecoder_defaultParameters() {
|
||||
fun decoder_processSilence_doesNotCrash() {
|
||||
val decoder = CwDecoder()
|
||||
assertEquals(8000, decoder.sampleRate)
|
||||
decoder.processBuffer(FloatArray(256) { 0f })
|
||||
assertEquals("", decoder.decodedTextFlow.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun cwDecoder_customParameters() {
|
||||
val decoder = CwDecoder(sampleRate = 11025, cwToneFreq = 600f)
|
||||
assertEquals(11025, decoder.sampleRate)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun resetDecoder_clearsState() {
|
||||
fun decoder_processNoise_doesNotCrash() {
|
||||
val decoder = CwDecoder()
|
||||
decoder.processBuffer(FloatArray(128) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() })
|
||||
decoder.processBuffer(FloatArray(256) { (Math.random() * 2 - 1).toFloat() * 0.1f })
|
||||
assertNotNull(decoder.decodedTextFlow.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun decoder_processTone_doesNotCrash() {
|
||||
val decoder = CwDecoder()
|
||||
for (i in 0..20) {
|
||||
decoder.processBuffer(FloatArray(256) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() })
|
||||
}
|
||||
assertNotNull(decoder.decodedTextFlow.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun decoder_reset() {
|
||||
val decoder = CwDecoder()
|
||||
decoder.processBuffer(FloatArray(256) { 1f })
|
||||
decoder.resetDecoder()
|
||||
assertEquals("", decoder.decodedTextFlow.value)
|
||||
assertEquals(0f, decoder.signalStrength.value, 0.001f)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun processBuffer_silence_doesNotCrash() {
|
||||
val decoder = CwDecoder()
|
||||
val silence = FloatArray(1024) { 0f }
|
||||
decoder.processBuffer(silence)
|
||||
assertEquals("", decoder.decodedTextFlow.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun processBuffer_noise_doesNotCrash() {
|
||||
val decoder = CwDecoder()
|
||||
val noise = FloatArray(1024) { (Math.random() * 2 - 1).toFloat() * 0.1f }
|
||||
decoder.processBuffer(noise)
|
||||
assertNotNull(decoder.decodedTextFlow.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun processBuffer_withFixedPitch_doesNotCrash() {
|
||||
fun decoder_withFixedPitch() {
|
||||
val decoder = CwDecoder(sampleRate = 8000, cwToneFreq = 700f)
|
||||
val buf = FloatArray(512) { (sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() }
|
||||
decoder.processBuffer(buf)
|
||||
assertNotNull(decoder.decodedTextFlow.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun cwDecoder_withFixedPitchBypassesAutoDetect() {
|
||||
val decoder = CwDecoder(sampleRate = 8000, cwToneFreq = 600f)
|
||||
assertEquals(600f, decoder.estimatedPitch.value)
|
||||
}
|
||||
|
||||
@Test
|
||||
fun resetDecoder_afterFixedPitch() {
|
||||
val decoder = CwDecoder(sampleRate = 8000, cwToneFreq = 700f)
|
||||
decoder.resetDecoder()
|
||||
assertEquals("", decoder.decodedTextFlow.value)
|
||||
// Pitch should still be locked at 700
|
||||
assertEquals(700f, decoder.estimatedPitch.value)
|
||||
}
|
||||
}
|
||||
Reference in new issue
Block a user