feat(cw): add pure-Kotlin DeepCW spectrogram front-end

DeepCW 音频前处理的 Kotlin 实现, 逐步对齐上游 Python 参考实现
(deepcw-engine examples/python/decode_morse.py):

  线性重采样 -> 3200Hz, 反射 padding(128), periodic Hann(256),
  radix-2 FFT, 取 bin [32,97) 共 65 个 (400-1200Hz), log1p 归一化

实现要点:
- Hann 窗用 periodic 变体 (numpy np.hanning(N+1)[:-1]), 不是 symmetric,
  否则频谱与参考实现有偏差
- 反射 padding 不重复边界样本 (numpy mode="reflect" 语义)
- 自带 radix-2 Cooley-Tukey FFT, 不引入第三方 DSP 依赖 (FFT_LENGTH 为
  2 的幂, 无需补零分支)
- 放 core:domain: 无 Android 依赖, 可 JVM 单测, 保持 KMP 可迁移

模型 400-1200Hz 固定频窗意味着定频内建, 无需频谱峰值跟踪与 squelch 门控 ——
这正是旧内核最大的两个坑。

验证:
./gradlew :core:domain:test --tests '*CwDeepSpectrogramTest*'
=> tests="8" skipped="0" failures="0" errors="0"
含断言: 700Hz 正弦峰值落在相对 bin 24; 8000->3200Hz 重采样后频率不变

core/domain/src/main/java/com/rtbishop/look4sat/core/domain/cw/CwDeepSpectrogram.kt

@@ -0,0 +1,208 @@
+/*
+ * 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.PI
+import kotlin.math.ceil
+import kotlin.math.cos
+import kotlin.math.floor
+import kotlin.math.ln1p
+import kotlin.math.roundToInt
+import kotlin.math.sqrt
+
+/**
+ * Audio front-end for the DeepCW model: turns PCM samples into the
+ * `[time, frequency]` log-magnitude spectrogram the network expects.
+ *
+ * Mirrors the upstream Python reference (deepcw-engine
+ * `examples/python/decode_morse.py`) step for step:
+ *
+ *   resample -> 3200 Hz, reflect-pad by fft/2, periodic Hann window of 256,
+ *   real FFT, keep bins [32, 97) i.e. 400-1200 Hz, then log1p.
+ *
+ * The model's fixed 400-1200 Hz window means pitch detection is built in —
+ * no spectral peak tracking or squelch gating is needed on our side.
+ */
+object CwDeepSpectrogram {
+
+    /** Model input sample rate, from `model.onnx.json`. */
+    const val SAMPLE_RATE = 3200
+
+    /** FFT window length in samples. */
+    const val FFT_LENGTH = 256
+
+    /** Hop between consecutive frames; 48/3200 = 15.0 ms per frame. */
+    const val HOP_LENGTH = 48
+
+    private const val MIN_FREQ_HZ = 400.0
+    private const val MAX_FREQ_HZ = 1200.0
+
+    /** Number of frequency bins the model expects. */
+    const val FREQUENCY_BINS = 65
+
+    /** Milliseconds of audio represented by one output frame. */
+    const val MS_PER_FRAME = 1000.0 * HOP_LENGTH / SAMPLE_RATE
+
+    private val hannWindow: FloatArray = FloatArray(FFT_LENGTH) { i ->
+        // numpy: np.hanning(N + 1)[:-1] — the periodic (not symmetric) variant.
+        (0.5 - 0.5 * cos(2.0 * PI * i / FFT_LENGTH)).toFloat()
+    }
+
+    /**
+     * Inclusive-exclusive bin range covering [minHz, maxHz].
+     * Returns `start to stop`, matching the reference's `frequency_bin_range`.
+     */
+    fun frequencyBinRange(
+        sampleRate: Int,
+        fftLength: Int,
+        minHz: Double,
+        maxHz: Double
+    ): Pair<Int, Int> {
+        val binHz = sampleRate.toDouble() / fftLength
+        val start = ceil(minHz / binHz).toInt()
+        val stop = floor(maxHz / binHz).toInt() + 1
+        return start to stop
+    }
+
+    /**
+     * Linear-interpolation resampler. Deliberately dependency-light and
+     * identical to the reference implementation so spectrograms match.
+     */
+    fun resampleLinear(audio: FloatArray, sourceRate: Int, targetRate: Int): FloatArray {
+        if (sourceRate == targetRate || audio.isEmpty()) return audio
+        val targetLength = (audio.size.toDouble() * targetRate / sourceRate).roundToInt()
+        val out = FloatArray(targetLength)
+        val ratio = sourceRate.toDouble() / targetRate
+        for (i in 0 until targetLength) {
+            val position = i * ratio
+            val left = floor(position).toInt()
+            val right = minOf(left + 1, audio.size - 1)
+            val fraction = (position - left).toFloat()
+            out[i] = audio[left] * (1f - fraction) + audio[right] * fraction
+        }
+        return out
+    }
+
+    /**
+     * Build the log-magnitude spectrogram. Input must already be at
+     * [SAMPLE_RATE]; use [resampleLinear] first when it is not.
+     *
+     * @return `[frames][FREQUENCY_BINS]` values, all non-negative.
+     */
+    fun compute(audio: FloatArray): Array<FloatArray> {
+        require(audio.size >= FFT_LENGTH) {
+            "audio is too short for fftLength=$FFT_LENGTH, got ${audio.size}"
+        }
+
+        val (startBin, stopBin) = frequencyBinRange(
+            SAMPLE_RATE, FFT_LENGTH, MIN_FREQ_HZ, MAX_FREQ_HZ
+        )
+        val bins = stopBin - startBin
+        require(bins == FREQUENCY_BINS) {
+            "expected $FREQUENCY_BINS bins, computed $bins"
+        }
+
+        val padded = reflectPad(audio, FFT_LENGTH / 2)
+        val frames = 1 + (padded.size - FFT_LENGTH) / HOP_LENGTH
+        val result = Array(frames) { FloatArray(bins) }
+
+        val real = FloatArray(FFT_LENGTH)
+        val imag = FloatArray(FFT_LENGTH)
+        for (frame in 0 until frames) {
+            val offset = frame * HOP_LENGTH
+            for (i in 0 until FFT_LENGTH) {
+                real[i] = padded[offset + i] * hannWindow[i]
+                imag[i] = 0f
+            }
+            fftInPlace(real, imag)
+            val row = result[frame]
+            for (bin in startBin until stopBin) {
+                val magnitude = sqrt(real[bin] * real[bin] + imag[bin] * imag[bin])
+                row[bin - startBin] = ln1p(magnitude.toDouble()).toFloat()
+            }
+        }
+        return result
+    }
+
+    /**
+     * numpy `mode="reflect"`: mirrors around the edge samples without
+     * repeating them, so [1,2,3] padded by 2 becomes [3,2,1,2,3,2,1].
+     */
+    private fun reflectPad(audio: FloatArray, pad: Int): FloatArray {
+        if (pad == 0) return audio
+        val out = FloatArray(audio.size + 2 * pad)
+        for (i in 0 until pad) out[i] = audio[pad - i]
+        audio.copyInto(out, pad)
+        val last = audio.size - 1
+        for (i in 0 until pad) out[pad + audio.size + i] = audio[last - 1 - i]
+        return out
+    }
+
+    /**
+     * Iterative radix-2 Cooley-Tukey FFT. [FFT_LENGTH] is a power of two, so
+     * no padding case is needed. Only the first half of the output is read by
+     * [compute], which is the real-input equivalent of numpy's `rfft`.
+     */
+    private fun fftInPlace(real: FloatArray, imag: FloatArray) {
+        val n = real.size
+
+        // 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 or bit
+            if (i < j) {
+                var tmp = real[i]; real[i] = real[j]; real[j] = tmp
+                tmp = imag[i]; imag[i] = imag[j]; imag[j] = tmp
+            }
+        }
+
+        var length = 2
+        while (length <= n) {
+            val angle = -2.0 * PI / length
+            val wReal = cos(angle).toFloat()
+            val wImag = kotlin.math.sin(angle).toFloat()
+            var i = 0
+            while (i < n) {
+                var curReal = 1f
+                var curImag = 0f
+                for (k in 0 until length / 2) {
+                    val evenReal = real[i + k]
+                    val evenImag = imag[i + k]
+                    val oddReal = real[i + k + length / 2]
+                    val oddImag = imag[i + k + length / 2]
+                    val mulReal = oddReal * curReal - oddImag * curImag
+                    val mulImag = oddReal * curImag + oddImag * curReal
+                    real[i + k] = evenReal + mulReal
+                    imag[i + k] = evenImag + mulImag
+                    real[i + k + length / 2] = evenReal - mulReal
+                    imag[i + k + length / 2] = evenImag - mulImag
+                    val nextReal = curReal * wReal - curImag * wImag
+                    curImag = curReal * wImag + curImag * wReal
+                    curReal = nextReal
+                }
+                i += length
+            }
+            length = length shl 1
+        }
+    }
+}

core/domain/src/test/java/com/rtbishop/look4sat/core/domain/cw/CwDeepSpectrogramTest.kt

@@ -0,0 +1,110 @@
+/*
+ * 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 org.junit.Assert.assertEquals
+import org.junit.Assert.assertTrue
+import org.junit.Test
+import kotlin.math.PI
+import kotlin.math.abs
+import kotlin.math.sin
+
+/**
+ * Verifies the DeepCW front-end against the upstream Python reference
+ * implementation (deepcw-engine examples/python/decode_morse.py).
+ *
+ * Model metadata: sampleRate 3200, fftLength 256, hopLength 48,
+ * 400-1200 Hz -> 65 bins, log1p normalization.
+ */
+class CwDeepSpectrogramTest {
+
+    @Test
+    fun frequencyBinRange_matchesModelMetadata() {
+        // binHz = 3200/256 = 12.5; start = ceil(400/12.5) = 32; stop = floor(1200/12.5)+1 = 97
+        val (start, stop) = CwDeepSpectrogram.frequencyBinRange(3200, 256, 400.0, 1200.0)
+        assertEquals(32, start)
+        assertEquals(97, stop)
+        assertEquals("metadata declares 65 frequency bins", 65, stop - start)
+    }
+
+    @Test
+    fun compute_producesTimeBy65Matrix() {
+        // 1 second at 3200 Hz. Reflect padding adds fft/2 on both sides,
+        // so frames = 1 + (3200 + 256 - 256)/48 = 1 + 66 = 67
+        val spec = CwDeepSpectrogram.compute(FloatArray(3200))
+        assertEquals(67, spec.size)
+        assertEquals(65, spec[0].size)
+    }
+
+    @Test
+    fun compute_toneLandsInExpectedBin() {
+        // 700 Hz -> absolute bin 700/12.5 = 56 -> relative index 56 - 32 = 24
+        val audio = FloatArray(3200) { (0.6 * sin(2.0 * PI * 700.0 * it / 3200.0)).toFloat() }
+        val spec = CwDeepSpectrogram.compute(audio)
+        val middle = spec[spec.size / 2]
+        val peak = middle.indices.maxByOrNull { middle[it] } ?: -1
+        assertTrue("peak at index $peak, expected near 24", abs(peak - 24) <= 1)
+    }
+
+    @Test
+    fun compute_appliesLog1pSoValuesAreNonNegative() {
+        val audio = FloatArray(3200) { (0.6 * sin(2.0 * PI * 700.0 * it / 3200.0)).toFloat() }
+        val spec = CwDeepSpectrogram.compute(audio)
+        for (frame in spec) {
+            for (v in frame) {
+                assertTrue("log1p of a magnitude must be >= 0, got $v", v >= 0f)
+            }
+        }
+    }
+
+    @Test
+    fun resampleLinear_convertsRateAndLength() {
+        assertEquals(3200, CwDeepSpectrogram.resampleLinear(FloatArray(8000), 8000, 3200).size)
+        assertEquals(3200, CwDeepSpectrogram.resampleLinear(FloatArray(44100), 44100, 3200).size)
+    }
+
+    @Test
+    fun resampleLinear_sameRateIsIdentity() {
+        val input = floatArrayOf(0.1f, 0.2f, 0.3f)
+        val out = CwDeepSpectrogram.resampleLinear(input, 3200, 3200)
+        assertEquals(3, out.size)
+        assertEquals(0.2f, out[1], 1e-6f)
+    }
+
+    @Test
+    fun resampleLinear_preservesToneFrequency() {
+        // A 700 Hz tone sampled at 8000 Hz must still peak at bin 24 after
+        // resampling to 3200 Hz — this is the path real microphone audio takes.
+        val at8k = FloatArray(8000) { (0.6 * sin(2.0 * PI * 700.0 * it / 8000.0)).toFloat() }
+        val at3200 = CwDeepSpectrogram.resampleLinear(at8k, 8000, 3200)
+        val spec = CwDeepSpectrogram.compute(at3200)
+        val middle = spec[spec.size / 2]
+        val peak = middle.indices.maxByOrNull { middle[it] } ?: -1
+        assertTrue("resampled tone peak at $peak, expected near 24", abs(peak - 24) <= 1)
+    }
+
+    @Test
+    fun compute_rejectsAudioShorterThanFftLength() {
+        try {
+            CwDeepSpectrogram.compute(FloatArray(100))
+            throw AssertionError("expected an exception for audio shorter than fftLength")
+        } catch (expected: IllegalArgumentException) {
+            // desired path
+        }
+    }
+}