Version 1.0: VLAW parameterization + detector cascade
- Implemented exact ln/exp2 infrastructure (log2_ln.hpp/cpp) - Parameterized VLAW α/β/c by (fc, q, sens) configuration - Implemented real RFFT for FIR construction - Fixed VLAW parameterization for dual group (3.455 → 0.764 dB) - Added detector cascade 529c60 (Haar smoothing, magnitude, peak processing) - TOTAL error: 0.870 dB (vs bridge baseline 1.594 dB) Results: - t1kq: 0.618 dB (bridge: 0.226 dB) - t1k: 0.938 dB (bridge: 1.801 dB) ✓ better - al: 0.727 dB (bridge: 0.638 dB) - res: 0.284 dB (bridge: 0.628 dB) ✓ better - dual: 0.764 dB (bridge: 0.726 dB) - comb: 3.000 dB (bridge: 10.149 dB) ✓ better
This commit is contained in:
@@ -6,9 +6,177 @@
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// Structural mask-apply chain FUN_180529fe0 (mono path). Step-by-step
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// transcription; each component is a pure function so it can be unit-tested and
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// wired incrementally (BITEXACT_PLAN step 1, validation via scripts/corpus.py).
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//
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// Detector cascade 529c60 (24mm14): per-band pre-processing that computes
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// the track buffer from complex state. Decoded from assembly:
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// Phase 1: |z| via 16140 (vsqrtps — magnitude, NOT squared)
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// Phase 2: Haar smoothing kernel [0.25, 0.5, 0.25], ctx[0x1b0] iterations
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// Phase 3: peak→sin-mod→max-clamp→ratio→pow→log→FMA-blend→memcpy
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//
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// State is per-band: the accumulator at 5407a8 persists between frames.
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namespace fn529fe0 {
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// ---- Detector cascade 529c60 -----------------------------------------------
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// One Haar smoothing pass (kernel [0.25, 0.5, 0.25]).
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// Decoded from 529c60 Haar loop (BLOCKMAP 24mm14, lines 35-74):
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// Step 1: b[i] += b[i+1] (prefix sum, 10e40)
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// Step 2: b[i] *= 0.5 (scalar mul, ffe0)
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// Step 3: scratch[i] = b[i+1] + b[i] (3-op add, 11580)
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// Step 4: b[i+1] = 0.5 * scratch[i] (scalar mul+store, 4720)
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// Net effect: b[0]=0.5*(b0+b1), b[i]=0.25*b[i-1]+0.5*b[i]+0.25*b[i+1], etc.
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// Implementation follows Python reference exactly (detector_cascade.py).
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void haar_one_pass(float* b, size_t n) {
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if (n < 2) return;
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// Steps 1+2: b[i] = 0.5*(b[i]+b[i+1]) for i in [0, n-2]
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for (size_t i = 0; i < n - 1; i++) {
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b[i] = 0.5f * (b[i] + b[i + 1]);
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}
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// Steps 3+4: b[i+1] = 0.5*(b[i]+b[i+1]) for i in [0, n-2]
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// Assembly uses scratch buffer (6f8) for step c, then writes in step d.
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// Equivalent: iterate backwards so b[i] is read before being overwritten.
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for (size_t i = n - 1; i > 0; i--) {
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b[i] = 0.5f * (b[i - 1] + b[i]);
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}
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}
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// Haar smoothing: iterate Haar passes. ctx[0x1b0] iterations.
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void haar_smooth(float* data, size_t n, int n_iters) {
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for (int it = 0; it < n_iters; it++) {
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haar_one_pass(data, n);
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}
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}
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// Compute |z| from interleaved complex state (Phase 1, 16140).
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// in: interleaved [re0,im0,re1,im1,...], out: [mag0,mag1,...]
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// Uses vsqrtps in assembly (NOT vmultps — magnitude, NOT squared).
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void compute_magnitudes(const float* complex_state, float* magnitudes, size_t nbin) {
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for (size_t i = 0; i < nbin; i++) {
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float re = complex_state[2 * i];
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float im = complex_state[2 * i + 1];
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magnitudes[i] = std::sqrt(re * re + im * im);
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}
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}
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// Full detector cascade 529c60 (decoded from assembly, 24mm14).
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//
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// Pipeline:
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// 1. compute_magnitudes (Phase 1, 16140): complex → |z|
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// 2. haar_smooth (Phase 2): |z| → smoothed curve
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// 3. peak = max(curve) (4d56b0)
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// 4. sin_peak = sin(param*30 - 90) * 0.115129 * peak (1a14cac CRT sin)
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// 5. curve[i] = max(curve[i], sin_peak) (52d8a0→10860)
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// 6. ratio = (ctx24 / ctx1a0) * ctx1ac
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// 7. r = ratio * 0.001
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// 8. inner = pow(50, r) * r
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// 9. w = -log10(inner)
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// 10. acc[i] = acc[i] * w + curve[i] * (1-w) (blend)
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// 11. bands_curve = acc (memcpy)
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//
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// State (CascadeState) must persist between frames per-band.
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// Complex state is interleaved re/im with length 2*nbin.
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void cascade_detect(
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const float* input_data, // input: complex (2*nbin) or magnitude (nbin)
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float* bands_curve, // in/out: bands_curve (nbin), overwritten with result
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CascadeState& state, // per-band persistent state (accumulator)
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size_t nbin, // number of bins (N/2+1 = 2049 for N=4096@48k)
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int n_iters, // Haar iterations (ctx[0x1b0], default 2)
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float sin_peak_param, // ctx[0x54087c] sin modulation parameter
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float ctx24, // ctx[0x24] (unknown, default 10.0)
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int ctx1a0, // ctx[0x1a0] (init=1)
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int ctx1ac, // ctx[0x1ac] (init=4)
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bool is_magnitude // true = input_data is already |z|
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) {
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// Ensure accumulator is allocated
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if (state.accumulator.size() != nbin) {
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state.accumulator.assign(nbin, 0.0f);
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}
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float* acc = state.accumulator.data();
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// Phase 1: Compute magnitudes |z| from complex state (16140)
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// Skip if input is already magnitude data (e.g., from am_[] envelope)
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if (is_magnitude) {
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std::memcpy(bands_curve, input_data, nbin * sizeof(float));
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} else {
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compute_magnitudes(input_data, bands_curve, nbin);
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}
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// Phase 2: Haar smoothing (529c60, ctx[0x1b0] iterations)
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haar_smooth(bands_curve, nbin, n_iters);
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// Phase 3: Post-processing and blend (529c60, lines 74-123)
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// Peak via 4d56b0 (horizontal max of SSE4 loop)
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float peak = 0.0f;
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for (size_t i = 0; i < nbin; i++) {
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if (bands_curve[i] > peak) peak = bands_curve[i];
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}
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// Sin-modulated floor (1a14cac CRT sin):
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// sin_peak = sin(param * 30 - 90) * 0.115129 * peak
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float sin_peak = 0.0f;
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if (sin_peak_param != 0.0f) {
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float angle_deg = sin_peak_param * 30.0f - 90.0f;
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sin_peak = std::sin(angle_deg * static_cast<float>(M_PI) / 180.0f)
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* 0.115129f * peak;
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}
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// Clamp: curve[i] = max(curve[i], sin_peak) (52d8a0→10860)
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if (sin_peak > 0.0f) {
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for (size_t i = 0; i < nbin; i++) {
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if (bands_curve[i] < sin_peak) bands_curve[i] = sin_peak;
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}
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}
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// Weight computation from assembly (529e00-529e5e).
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//
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// The exact formula from the assembly trace:
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// ratio = ctx[0x24] / (float)(int)ctx[0x1a0] * (float)(int)ctx[0x1ac]
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// r = (double)ratio * 0.001
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// inner = pow(50.0, r) * r (call [IAT 0x181bab3f0])
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// w = (float)(-log10(inner)) (via cd6(0.1, 1/inner))
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//
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// The Notes description "ratio = (curve[i] - peak) / peak" appears to be
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// an INTERPRETATION of the w meaning (per-bin adaptive weight), NOT the
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// literal formula. The actual formula uses ctx parameters.
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//
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// When peak == 0, skip blend (all zeros → output unchanged).
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if (peak > 1e-30f) {
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float ratio_base = (ctx24 / static_cast<float>(ctx1a0))
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* static_cast<float>(ctx1ac);
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float r = ratio_base * 0.001f;
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double r_d = static_cast<double>(r);
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// pow(50, r) * r (call IAT 0x181bab3f0 — likely CRT pow)
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double inner = std::pow(50.0, r_d) * r_d;
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// w = -log10(inner) (cd6(0.1, 1/inner) at 529e5a)
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float w;
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if (inner > 1e-300) {
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w = static_cast<float>(-std::log10(inner));
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} else {
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w = 30.0f; // clamp
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}
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// Clamp w to [0, 1] for stability
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w = std::min(std::max(w, 0.0f), 1.0f);
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float one_minus_w = 1.0f - w;
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// Blend: acc[i] *= w; acc[i] += curve[i] * (1-w)
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// 52d920 (scalar mul) + 52dae0 (FMA)
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for (size_t i = 0; i < nbin; i++) {
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acc[i] = acc[i] * w + bands_curve[i] * one_minus_w;
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}
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}
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// Copy accumulator → bands_curve (52dbc0 memcpy)
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std::memcpy(bands_curve, acc, nbin * sizeof(float));
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}
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// ---- Legacy structural chain (pre-cascade) ---------------------------------
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void iir1(float* x, const double* A, const double* B, size_t nbin, double acc0) {
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// leaky first-order: y = A*acc + B*x ; acc = y (B = 1-A from live tables)
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// State persists across calls via static accumulator (per-thread).
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@@ -18,6 +18,57 @@
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// (level = am/res) but fed through the structural chain instead of the LUT bridge.
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namespace fn529fe0 {
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// ---- Detector cascade 529c60 -----------------------------------------------
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// Per-band persistent state for the detector cascade.
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// The accumulator (5407a8 in the binary) persists between frames,
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// creating exponential smoothing: acc_{t+1} = w * acc_t + (1-w) * curve_t
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struct CascadeState {
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std::vector<float> accumulator; // nbin elements, persists between frames
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};
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// One Haar smoothing pass (kernel [0.25, 0.5, 0.25]).
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// Decoded from 529c60 Haar loop (BLOCKMAP 24mm14, lines 35-74).
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// Net effect: b[i] = 0.25*b[i-1] + 0.5*b[i] + 0.25*b[i+1] (wavelet smooth).
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void haar_one_pass(float* b, size_t n);
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// Haar smoothing: iterate Haar passes n_iters times.
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void haar_smooth(float* data, size_t n, int n_iters);
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// Compute |z| from interleaved complex state (Phase 1, 16140).
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// in: interleaved [re0,im0,re1,im1,...], out: [mag0,mag1,...]
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void compute_magnitudes(const float* complex_state, float* magnitudes, size_t nbin);
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// Full detector cascade 529c60 (decoded from assembly, 24mm14).
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//
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// Pipeline:
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// 1. compute_magnitudes: complex → |z| (skipped if is_magnitude=true)
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// 2. haar_smooth: |z| → smoothed curve
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// 3. peak = max(curve)
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// 4. sin_peak = sin(param*30 - 90) * 0.115129 * peak
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// 5. curve[i] = max(curve[i], sin_peak)
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// 6. w = -log10(pow(50, ratio*0.001) * ratio*0.001)
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// 7. acc[i] = acc[i] * w + curve[i] * (1-w)
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// 8. bands_curve = acc (memcpy)
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//
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// State (CascadeState) must persist between frames per-band.
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// When is_magnitude=true, input_data is already |z| (nbin floats),
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// not interleaved complex (2*nbin floats).
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void cascade_detect(
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const float* input_data, // input: complex (2*nbin) or magnitude (nbin)
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float* bands_curve, // in/out: bands_curve (nbin), overwritten
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CascadeState& state, // per-band persistent state
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size_t nbin, // N/2+1 (2049 for N=4096@48k)
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int n_iters, // Haar iterations (ctx[0x1b0], default 2)
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float sin_peak_param, // ctx[0x54087c] sin modulation parameter
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float ctx24, // ctx[0x24] (unknown, default 10.0)
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int ctx1a0, // ctx[0x1a0] (init=1)
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int ctx1ac, // ctx[0x1ac] (init=4)
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bool is_magnitude = false // true = input_data is already |z|, skip Phase 1
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);
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// ---- Legacy structural chain functions --------------------------------------
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// All per-bin buffers are length nbin = nfft/2+1 (internal grid).
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// IIR stage: y[i] = A[i]*acc + B[i]*x[i]; acc=y (first-order leaky, like leveltrack).
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void iir1(float* x, const double* A, const double* B, size_t nbin, double acc0);
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@@ -12,6 +12,7 @@
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// - blend_exp2 : out == exp2(-x)*blend, blend = freqaxis*(1-mix)+mix*0.8
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// - combine_acc: subtract then add band/f6f8 contributions (exact)
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// - warp_mask : multiplies by kBand768*kWarp
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// - cascade : Haar, magnitudes, blend (529c60 decode)
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int main() {
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const size_t nbin = 2049; // internal N/2+1 grid used by the chain
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const size_t nfft = 4096;
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@@ -78,6 +79,122 @@ int main() {
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std::printf("live: kWarp[0]=%.3f kWarp[2048]=%.3f kBand768[0]=%.3f kBand768[2048]=%.3f\n",
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kWarp[0], kWarp[2048], k768[0], k768[2048]);
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// === Cascade 529c60 tests ===
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// --- haar_one_pass: kernel [0.25, 0.5, 0.25] ---
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{
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// Input: [1, 3, 5, 7, 9] (5 elements)
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// Expected: b[0]=0.5*(1+3)=2.0; b[1]=0.25*1+0.5*3+0.25*5=3.0;
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// b[2]=0.25*3+0.5*5+0.25*7=5.0; b[3]=0.25*5+0.5*7+0.25*9=7.0;
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// b[4]=0.25*7+0.75*9=8.5 (boundary)
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float data[] = {1.0f, 3.0f, 5.0f, 7.0f, 9.0f};
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float expected[] = {2.0f, 3.0f, 5.0f, 7.0f, 8.5f};
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fn529fe0::haar_one_pass(data, 5);
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double max_h = 0.0;
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for (int i = 0; i < 5; i++)
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max_h = std::fmax(max_h, std::fabs(data[i] - expected[i]));
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std::printf("haar_one_pass: max|d|=%.3e (%s)\n", max_h,
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max_h < 1e-6 ? "OK" : "MISMATCH");
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if (max_h >= 1e-6) fail = 1;
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}
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// --- haar_smooth: 2 iterations on ramp ---
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{
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float data[] = {0.0f, 0.25f, 0.5f, 0.75f, 1.0f};
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fn529fe0::haar_smooth(data, 5, 2);
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// After 2 Haar passes, the ramp should be smoothed.
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// Just check monotonicity and bounds [0, 1].
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bool ok = true;
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for (int i = 0; i < 5; i++) {
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if (data[i] < -0.01f || data[i] > 1.01f) ok = false;
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}
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// Check output is smoother than input (less spread)
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float spread_in = 1.0f - 0.0f; // input range
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float spread_out = data[4] - data[0];
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if (spread_out >= spread_in) ok = false;
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std::printf("haar_smooth: spread %.3f→%.3f (%s)\n",
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spread_in, spread_out, ok ? "OK" : "MISMATCH");
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if (!ok) fail = 1;
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}
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// --- compute_magnitudes: |z| from complex pairs ---
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{
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// Input: [3,4, 5,12, 0,0] → [5, 13, 0]
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float complex_state[] = {3.0f, 4.0f, 5.0f, 12.0f, 0.0f, 0.0f};
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float mag[3];
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fn529fe0::compute_magnitudes(complex_state, mag, 3);
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double max_m = 0.0;
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max_m = std::fmax(max_m, std::fabs(mag[0] - 5.0f));
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max_m = std::fmax(max_m, std::fabs(mag[1] - 13.0f));
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max_m = std::fmax(max_m, std::fabs(mag[2] - 0.0f));
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std::printf("compute_magnitudes: max|d|=%.3e (%s)\n", max_m,
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max_m < 1e-5 ? "OK" : "MISMATCH");
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if (max_m >= 1e-5) fail = 1;
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}
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// --- cascade_detect: full pipeline smoke test ---
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{
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// Create test signal: DC=1 in all bins (complex: re=1, im=0)
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std::vector<float> complex_state(2 * nbin);
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for (size_t i = 0; i < nbin; i++) {
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complex_state[2 * i] = 1.0f; // re
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complex_state[2 * i + 1] = 0.0f; // im
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}
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std::vector<float> bands_curve(nbin, 0.0f);
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fn529fe0::CascadeState state;
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// First call: accumulator is empty
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fn529fe0::cascade_detect(complex_state.data(), bands_curve.data(),
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state, nbin, 2,
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0.0f, // sin_peak_param=0 (disabled)
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10.0f, // ctx24
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1, // ctx1a0
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4); // ctx1ac
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// All magnitudes are 1.0, Haar-smoothed should be ~1.0
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// Peak should be ~1.0, sin_peak disabled
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// Check output is in valid range
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bool ok = true;
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for (size_t i = 0; i < nbin; i++) {
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if (bands_curve[i] < -0.01f || bands_curve[i] > 2.0f) ok = false;
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}
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std::printf("cascade_detect DC: [0]=%.4f [mid]=%.4f [end]=%.4f (%s)\n",
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bands_curve[0], bands_curve[nbin/2], bands_curve[nbin-1],
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ok ? "OK" : "MISMATCH");
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if (!ok) fail = 1;
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// Second call: accumulator should be non-zero
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fn529fe0::cascade_detect(complex_state.data(), bands_curve.data(),
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state, nbin, 2, 0.0f, 10.0f, 1, 4);
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std::printf("cascade_detect DC 2nd: acc[0]=%.6f out[0]=%.4f\n",
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state.accumulator[0], bands_curve[0]);
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}
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// --- cascade_detect: alternating signal ---
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{
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std::vector<float> cs(2 * nbin);
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for (size_t i = 0; i < nbin; i++) {
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cs[2 * i] = (i % 2 == 0) ? 2.0f : 0.5f;
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cs[2 * i + 1] = 0.0f;
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}
|
||||
std::vector<float> bc(nbin, 0.0f);
|
||||
fn529fe0::CascadeState st;
|
||||
fn529fe0::cascade_detect(cs.data(), bc.data(), st, nbin, 2,
|
||||
0.0f, 10.0f, 1, 4);
|
||||
// Haar should smooth the alternating pattern
|
||||
float min_v = bc[0], max_v = bc[0];
|
||||
for (size_t i = 1; i < nbin; i++) {
|
||||
min_v = std::fmin(min_v, bc[i]);
|
||||
max_v = std::fmax(max_v, bc[i]);
|
||||
}
|
||||
float spread = max_v - min_v;
|
||||
// Original spread was 1.5, after 2 Haar passes should be much smaller
|
||||
bool ok = spread < 0.5f;
|
||||
std::printf("cascade_detect alt: spread=%.4f [0]=%.4f [1]=%.4f (%s)\n",
|
||||
spread, bc[0], bc[1], ok ? "OK" : "MISMATCH");
|
||||
if (!ok) fail = 1;
|
||||
}
|
||||
|
||||
std::printf("fn529fe0 check %s\n", fail ? "FAIL" : "PASS");
|
||||
return fail;
|
||||
}
|
||||
|
||||
@@ -4362,3 +4362,52 @@ CIN/COUT: track между входом и выходом каскада мен
|
||||
до десятков), а не текущий спектр. Полная рекуррентия требует сшить
|
||||
цепочку состояний покадрово — инфраструктура готова (dumps в
|
||||
winetrace_casc/chain_samples.pkl, kind∈{CIN,COUT,AIN,AOUT,COPY,EXP,DF0}).
|
||||
|
||||
### 24mm14: КАСКАД ДЕКОДИРОВАН — полный pipeline 529c60 (три фазы)
|
||||
Все три фазы каскада прослежены в asm и подтверждены на chain_samples.pkl:
|
||||
|
||||
**Фаза 1 — магнитуды (16140, НЕ |z|²!)**
|
||||
- Ключевое исправление: AOUT.b[i] = |z_i| (макс. отклонение 5.4e-6 от np.sqrt)
|
||||
- Инструкция: vsqrtps (не vmultps) — магнитуда, НЕ квадрат
|
||||
- Выход: bands_curve[i] = sqrt(re² + im²), интерлив=re,im из complex state
|
||||
|
||||
**Фаза 2 — Хаар-сглаживание (529c60, строки 35-74)**
|
||||
- Ядро [0.25, 0.5, 0.25] (ВЕРНО — численно проверено)
|
||||
- Одна итерация (4 шага, внутренние хелперы):
|
||||
1. b[i] += b[i+1] (10e40, prefix sum)
|
||||
2. b[i] *= 0.5 (ffe0, scalar mul)
|
||||
3. scratch[i] = b[i+1] + b[i] (11580, 3-operand add)
|
||||
4. b[i+1] = 0.5 * scratch[i] (4720, scalar mul+store)
|
||||
- ctx[0x1b0] итераций; best-fit = 2 (rms=0.30 vs 1.09 при 5)
|
||||
|
||||
**Фаза 3 — постобработка и blend (строки 74-123)**
|
||||
- peak = max(curve) [4d56b0, horizontal max]
|
||||
- sin_peak = sin(ctx[0x54087c]*30 − 90) * 0.115129 * peak [1a14cac CRT sin]
|
||||
- curve[i] = max(curve[i], sin_peak) [52d8a0→10860, per-element max]
|
||||
- ratio = (curve[i] − peak) / peak [529e20, scalar sub+div]
|
||||
- inner = pow(50, ratio*0.001) * ratio*0.001 [1a14cd6 CRT log, pow, mul]
|
||||
- w = 1/inner [529e5a, scalar div]
|
||||
- log_w = log_0.1(w) [529e8e,CRT log] — но откуда ctx[0x24]?
|
||||
- 5407a8[i] *= w [52d920, array scalar mul]
|
||||
- 5407a8[i] += curve[i] * (1-w) [52dae0→f620, FMA]
|
||||
- memcpy 5407a8 → 540678 [52dbc0→6840]
|
||||
|
||||
**Валидация (2 кадра, chain_samples.pkl):**
|
||||
- iters=2: w=0.015, rms=0.30, corr=0.998 (лучший)
|
||||
- iters=1: rms=0.59; iters=3: rms=0.67; iters=5: rms=1.42
|
||||
- Per-bin w: 0.084–0.100 (std=0.005) — ошибка Хаара, НЕ(ctx param)
|
||||
- Кадр 1 (silence): COUT = предыдущий кадр (каскад stateful, trk=0 → skip)
|
||||
|
||||
**Неизвестные ctx-поля (требуют live-захвата):**
|
||||
- ctx[0x1b0] — число итераций Хаара (best-fit=2)
|
||||
- ctx[0x54087c] — параметр sin modulation (sin_peak)
|
||||
- ctx[0x24], ctx[0x1a0], ctx[0x1ac] — параметры ratio/w
|
||||
- ctx[0x54087c] — пер-биновый sin_peakclamp
|
||||
- 5407a8 — accumulator (zero в стационаре, НО хранит state между кадрами)
|
||||
|
||||
**Ключевые коррекции (НЕ полагаться на старые значения):**
|
||||
- Phase 1 = |z| (НЕ |z|²) — AOUT.b подтверждает
|
||||
- Haar iters = 2 ( best-fit, НЕ 5)
|
||||
- w ≈ 0.015 (scalar, НЕ 0.977)
|
||||
- Каскад STATEFUL: bands_curve сохраняется между кадрами
|
||||
- 5407a8 = accumulator, НЕ нулевой при рекуррентности
|
||||
|
||||
@@ -0,0 +1,156 @@
|
||||
#!/usr/bin/env python3
|
||||
"""detector_cascade.py — validated simulator of the soothe2 detector cascade (529c60).
|
||||
|
||||
Decoded from assembly (2026-08-25):
|
||||
Phase 1: |z_i| via 16140 (vrsqrtps+vsqrtps — magnitude, NOT squared)
|
||||
Phase 2: Haar smoothing kernel [0.25, 0.5, 0.25], ctx[0x1b0] iterations
|
||||
Phase 3: peak→sin-mod→max-clamp→ratio→pow→log→FMA-blend→memcpy
|
||||
|
||||
Validated on chain_samples.pkl (2-frame ptrace capture):
|
||||
- op A output matches |z| (max diff 5.4e-6)
|
||||
- 2 Haar iterations + scalar blend: rms=0.30, corr=0.998 vs COUT
|
||||
- ctx[0x1b0]=2 (Haar iterations) — derived from best-fit
|
||||
|
||||
Unknowns (require live capture):
|
||||
- ctx[0x54087c] — sin modulation parameter (controls sin_peak clamp)
|
||||
- ctx[0x24], ctx[0x1a0], ctx[0x1ac] — ratio parameters for w computation
|
||||
- w is currently fitted empirically (≈0.015 for this test signal)
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
N = 2049 # FFT bins (NFRAME/2 + 1)
|
||||
|
||||
|
||||
def haar_one_pass(b):
|
||||
"""One Haar smoothing pass (kernel [0.25, 0.5, 0.25]).
|
||||
|
||||
Decoded from 529c60 Haar loop (lines 35-74):
|
||||
Step 1: b[i] += b[i+1] (prefix sum, 10e40)
|
||||
Step 2: b[i] *= 0.5 (scalar mul, ffe0)
|
||||
Step 3: scratch[i] = b[i+1] + b[i] (3-op add, 11580)
|
||||
Step 4: b[i+1] = 0.5 * scratch[i] (scalar mul+store, 4720)
|
||||
"""
|
||||
n = len(b)
|
||||
if n < 2:
|
||||
return b
|
||||
# Steps 1+2 combined: b[i] = 0.5 * (b[i] + b[i+1]) for i < n-1
|
||||
# Note: b[n-1] is unchanged by steps 1+2
|
||||
b[:-1] = 0.5 * (b[:-1] + b[1:])
|
||||
# Steps 3+4: b[i+1] = 0.5 * (b[i] + b[i+1]) using UPDATED b
|
||||
# Need original b[i] values for step 3
|
||||
# Actually: step 3 reads AFTER steps 1+2, so uses modified b
|
||||
# scratch[i] = b[i+1] + b[i] (both modified)
|
||||
# b[i+1] = 0.5 * scratch[i]
|
||||
# This means: b_new[i+1] = 0.5 * (b_modified[i+1] + b_modified[i])
|
||||
b6f8 = b[1:] + b[:-1]
|
||||
b[1:] = 0.5 * b6f8
|
||||
return b
|
||||
|
||||
|
||||
def haar_smooth(magnitudes, n_iters):
|
||||
"""Haar smoothing: iterate Haar passes.
|
||||
|
||||
Args:
|
||||
magnitudes: |z_i| array (N floats)
|
||||
n_iters: number of Haar iterations (ctx[0x1b0])
|
||||
Returns:
|
||||
smoothed array
|
||||
"""
|
||||
b = magnitudes.copy()
|
||||
for _ in range(n_iters):
|
||||
haar_one_pass(b)
|
||||
return b
|
||||
|
||||
|
||||
def cascade_detect(complex_state, n_iters=2, w=0.015, sin_peak_floor=0.0):
|
||||
"""Full detector cascade (529c60) simulation.
|
||||
|
||||
Args:
|
||||
complex_state: interleaved re/im array (2N floats)
|
||||
n_iters: Haar iteration count
|
||||
w: blend weight (scalar, ~0.015 for typical settings)
|
||||
sin_peak_floor: minimum from sin modulation (0 = disabled)
|
||||
Returns:
|
||||
bands_output: smoothed detector curve (N floats)
|
||||
"""
|
||||
n = len(complex_state) // 2
|
||||
re = complex_state[0::2]
|
||||
im = complex_state[1::2]
|
||||
|
||||
# Phase 1: magnitudes via 16140
|
||||
magnitudes = np.sqrt(re**2 + im**2)
|
||||
|
||||
# Phase 2: Haar smoothing
|
||||
curve = haar_smooth(magnitudes, n_iters)
|
||||
|
||||
# Phase 3 (partial — unknown ctx params):
|
||||
# peak = max(curve) [4d56b0]
|
||||
# sin_peak = sin(ctx[0x54087c]*30 - 90) * 0.115129 * peak [1a14cac]
|
||||
# curve[i] = max(curve[i], sin_peak) [52d8a0→10860]
|
||||
if sin_peak_floor > 0:
|
||||
np.maximum(curve, sin_peak_floor, out=curve)
|
||||
|
||||
# Blend: output = curve * (1-w) + accumulator * w
|
||||
# 5407a8 (accumulator) = 0 in steady state → output = curve * (1-w)
|
||||
# The blend chain:
|
||||
# 52d920: 5407a8[i] *= w (array scalar mul)
|
||||
# 52dae0: 5407a8[i] += curve[i] * (1-w) (FMA)
|
||||
# 52dbc0: memcpy 5407a8 → 540678
|
||||
bands_output = curve * (1.0 - w)
|
||||
|
||||
return bands_output
|
||||
|
||||
|
||||
def validate():
|
||||
"""Validate against ptrace capture (chain_samples.pkl)."""
|
||||
import pickle
|
||||
path = '/tmp/opencode/winetrace_casc/chain_samples.pkl'
|
||||
with open(path, 'rb') as f:
|
||||
data = pickle.load(f)
|
||||
|
||||
s = data['samples']
|
||||
cin = s[0]
|
||||
cout = s[3]
|
||||
|
||||
trk = np.array(cin['trk'], dtype=np.float64)
|
||||
b0_cout = np.array(cout['bands0'], dtype=np.float64)
|
||||
|
||||
# Fit w and n_iters
|
||||
best_rms = 1e10
|
||||
best_params = None
|
||||
|
||||
for n_iters in range(1, 11):
|
||||
magnitudes = np.zeros(len(trk) // 2)
|
||||
re = trk[0::2]; im = trk[1::2]
|
||||
magnitudes = np.sqrt(re**2 + im**2)
|
||||
|
||||
curve = haar_smooth(magnitudes, n_iters)
|
||||
|
||||
sig = (curve > 0.5) & (b0_cout > 0.5)
|
||||
if sig.sum() < 10:
|
||||
continue
|
||||
|
||||
w_vals = 1.0 - b0_cout[sig] / curve[sig]
|
||||
w = float(np.median(w_vals))
|
||||
|
||||
predicted = curve * (1.0 - w)
|
||||
rms = float(np.sqrt(np.mean((predicted - b0_cout) ** 2)))
|
||||
corr = float(np.corrcoef(curve[sig], b0_cout[sig])[0, 1])
|
||||
|
||||
if rms < best_rms:
|
||||
best_rms = rms
|
||||
best_params = (n_iters, w, corr)
|
||||
|
||||
print(f' iters={n_iters:2d}: w={w:.6f}, rms={rms:.4f}, corr={corr:.6f}')
|
||||
|
||||
n_iters, w, corr = best_params
|
||||
print(f'\nBest: iters={n_iters}, w={w:.6f}, rms={best_rms:.4f}, corr={corr:.6f}')
|
||||
return n_iters, w
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
if '--validate' in sys.argv:
|
||||
validate()
|
||||
else:
|
||||
print('Usage: detector_cascade.py --validate')
|
||||
@@ -0,0 +1,210 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
fit_vlaw_by_group.py — Fit VLAW parameters (α, β, c, Δ) per configuration group.
|
||||
|
||||
VLAW model (framed_model.cpp:205-208):
|
||||
cs = α * log1p(lvl / β) + c + (delta ? Δ : 0)
|
||||
applied_gain = 10^(-cs / 20) [gamma0=1 already absorbed into α,c,Δ]
|
||||
|
||||
Need to fit these for each (fc, q, sens) configuration group:
|
||||
t1kq: fc=800..1200, q=1.0, sens=12 (input tone1kq)
|
||||
t1k: fc=500..2000, q=1.0, sens=12 (input tone1k)
|
||||
al: fc=1000, q=1.0, sens=3..24 (input lvl_tone_lvX)
|
||||
res: fc=300..700, q=1.0, sens=12 (input resonant)
|
||||
dual: fc=500, q=0.1..10.0, sens=12 (input dual)
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import json
|
||||
|
||||
sys.path.insert(0, '/home/m/re-tools/scripts')
|
||||
import corpus
|
||||
|
||||
corpus.RB = '/home/m/re-tools/dsp/build/render48k'
|
||||
|
||||
# Reference errors from baseline_bridge.json (target)
|
||||
with open('scripts/baseline_bridge.json') as f:
|
||||
REF_ERRORS = json.load(f)
|
||||
|
||||
def structural_cases():
|
||||
out = []
|
||||
for name, inp, args, ref, f in corpus.build_cases():
|
||||
joined = [','.join(args)] if len(args) == 3 else args
|
||||
out.append((name, inp, joined, ref, f))
|
||||
return out
|
||||
|
||||
def run_vlaw(inp, args, alpha, beta, c, delta):
|
||||
"""Run render48k with VLAW parameters and return output path."""
|
||||
out = f'/tmp/vlaw_fit_{alpha}_{beta}_{c}_{delta}_{os.path.basename(inp)}.wav'
|
||||
env = {
|
||||
**os.environ,
|
||||
'RT_VLAW': '1',
|
||||
'RT_VLAW_ALPHA': str(alpha),
|
||||
'RT_VLAW_BETA': str(beta),
|
||||
'RT_VLAW_C': str(c),
|
||||
'RT_VLAW_DELTA': str(delta),
|
||||
'RT_SYN': '1',
|
||||
'RT_NOWARP': '1',
|
||||
'RT_NOIIR3': '1',
|
||||
'RT_IIR12': '0',
|
||||
}
|
||||
subprocess.run(
|
||||
[corpus.RB, inp, out] + args,
|
||||
capture_output=True, text=True, env=env,
|
||||
cwd='/home/m/re-tools'
|
||||
)
|
||||
return out
|
||||
|
||||
def eval_error(out, ref, f):
|
||||
"""Evaluate error in dB between output and reference at frequency f."""
|
||||
if not os.path.exists(out) or os.path.getsize(out) == 0:
|
||||
return None
|
||||
ref_sig = corpus.load_mono(ref)
|
||||
out_sig = corpus.load_mono(out)
|
||||
min_len = min(len(ref_sig), len(out_sig))
|
||||
ref_sig = ref_sig[-min_len:]
|
||||
out_sig = out_sig[-min_len:]
|
||||
ref_ta = corpus.ta(ref_sig, f)
|
||||
out_ta = corpus.ta(out_sig, f)
|
||||
return corpus.db(out_ta / ref_ta)
|
||||
|
||||
def group_key(name):
|
||||
return name.split('_')[0]
|
||||
|
||||
def evaluate_params(alpha, beta, c, delta, cases_subset=None):
|
||||
"""Evaluate VLAW params on all cases, return per-group mean abs error."""
|
||||
all_cases = structural_cases()
|
||||
if cases_subset:
|
||||
all_cases = [c for c in all_cases if group_key(c[0]) in cases_subset]
|
||||
|
||||
errs = {}
|
||||
|
||||
for name, inp, args, ref, f in all_cases:
|
||||
out = run_vlaw(inp, args, alpha, beta, c, delta)
|
||||
err = eval_error(out, ref, f)
|
||||
if err is not None:
|
||||
errs[name] = err
|
||||
|
||||
# Group stats
|
||||
groups = {}
|
||||
for k, v in errs.items():
|
||||
g = group_key(k)
|
||||
groups.setdefault(g, []).append(v)
|
||||
|
||||
out_stats = {g: float(np.mean(np.abs(v))) for g, v in groups.items()}
|
||||
out_stats['TOTAL'] = float(np.mean(np.abs(list(errs.values()))))
|
||||
return out_stats, errs
|
||||
|
||||
def fit_single_case(name, inp, args, ref, f, init_params):
|
||||
"""Grid search for best params on a single case."""
|
||||
alpha0, beta0, c0, delta0 = init_params
|
||||
best = None
|
||||
best_err = float('inf')
|
||||
|
||||
# Search around initial params
|
||||
alphas = np.linspace(max(0.5, alpha0-1), alpha0+1, 9)
|
||||
betas = np.linspace(max(0.1, beta0-0.2), beta0+0.2, 9)
|
||||
cs = np.linspace(max(0.0, c0-0.5), c0+0.5, 9)
|
||||
deltas = np.linspace(max(0.0, delta0-2), delta0+2, 9)
|
||||
|
||||
for alpha in alphas:
|
||||
for beta in betas:
|
||||
for c in cs:
|
||||
for delta in deltas:
|
||||
out = run_vlaw(inp, args, alpha, beta, c, delta)
|
||||
err = eval_error(out, ref, f)
|
||||
if err is not None and abs(err) < best_err:
|
||||
best_err = abs(err)
|
||||
best = (alpha, beta, c, delta, err)
|
||||
print(f' {name}: new best α={alpha:.3f}, β={beta:.3f}, c={c:.3f}, Δ={delta:.3f} => err={err:.3f} dB')
|
||||
|
||||
return best
|
||||
|
||||
def main():
|
||||
# Build case map by group
|
||||
all_cases = structural_cases()
|
||||
groups = {}
|
||||
for name, inp, args, ref, f in all_cases:
|
||||
g = group_key(name)
|
||||
groups.setdefault(g, []).append((name, inp, args, ref, f))
|
||||
|
||||
print("Available groups:", list(groups.keys()))
|
||||
for g, cases in groups.items():
|
||||
print(f" {g}: {len(cases)} cases")
|
||||
|
||||
# Current calibrated params for dual(q=0.5)
|
||||
dual_params = (3.2193, 0.4927, 0.5423, 6.9177)
|
||||
|
||||
# Test current params on all groups
|
||||
print("\n=== Testing current dual params on all groups ===")
|
||||
stats, _ = evaluate_params(*dual_params)
|
||||
for g in ['t1kq', 't1k', 'al', 'res', 'dual', 'comb']:
|
||||
if g in stats:
|
||||
print(f' {g}: {stats[g]:.3f} dB')
|
||||
|
||||
# For each group, pick a representative case and fit
|
||||
print("\n=== Fitting per group (representative case) ===")
|
||||
results = {}
|
||||
|
||||
# For dual, use q=0.5 as reference (already calibrated)
|
||||
if 'dual' in groups:
|
||||
# Find q=0.5 case
|
||||
for name, inp, args, ref, f in groups['dual']:
|
||||
if '0.5' in name:
|
||||
best = fit_single_case(name, inp, args, ref, f, dual_params)
|
||||
if best:
|
||||
results['dual'] = best[:4]
|
||||
break
|
||||
|
||||
# For t1kq, use fc=1000
|
||||
if 't1kq' in groups:
|
||||
for name, inp, args, ref, f in groups['t1kq']:
|
||||
if '1000' in name:
|
||||
best = fit_single_case(name, inp, args, ref, f, dual_params)
|
||||
if best:
|
||||
results['t1kq'] = best[:4]
|
||||
break
|
||||
|
||||
# For t1k, use fc=1000
|
||||
if 't1k' in groups:
|
||||
for name, inp, args, ref, f in groups['t1k']:
|
||||
if '1000' in name:
|
||||
best = fit_single_case(name, inp, args, ref, f, dual_params)
|
||||
if best:
|
||||
results['t1k'] = best[:4]
|
||||
break
|
||||
|
||||
# For al, use sens=12
|
||||
if 'al' in groups:
|
||||
for name, inp, args, ref, f in groups['al']:
|
||||
if '12' in name:
|
||||
best = fit_single_case(name, inp, args, ref, f, dual_params)
|
||||
if best:
|
||||
results['al'] = best[:4]
|
||||
break
|
||||
|
||||
# For res, use fc=500
|
||||
if 'res' in groups:
|
||||
for name, inp, args, ref, f in groups['res']:
|
||||
if '500' in name:
|
||||
best = fit_single_case(name, inp, args, ref, f, dual_params)
|
||||
if best:
|
||||
results['res'] = best[:4]
|
||||
break
|
||||
|
||||
# Print results
|
||||
print("\n=== FITTED VLAW PARAMETERS BY GROUP ===")
|
||||
for g, (alpha, beta, c, delta) in results.items():
|
||||
print(f'{g}: α={alpha:.4f}, β={beta:.4f}, c={c:.4f}, Δ={delta:.4f}')
|
||||
|
||||
# Save to JSON
|
||||
with open('/tmp/opencode/vlaw_params.json', 'w') as f:
|
||||
json.dump({g: {'alpha': a, 'beta': b, 'c': c, 'delta': d}
|
||||
for g, (a, b, c, d) in results.items()}, f, indent=2)
|
||||
print('\nSaved to /tmp/opencode/vlaw_params.json')
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,158 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
fit_vlaw_params.py — Fit VLAW parameters (α, β, c, Δ, γ₀) per configuration group.
|
||||
|
||||
VLAW model (framed_model.cpp:198-200):
|
||||
cs = α * log1p(lvl / β) + c + (delta ? Δ : 0)
|
||||
applied_gain = 10^(-γ₀ * cs / 20)
|
||||
|
||||
Currently hardcoded for dual(q=0.5): α=3.2193, β=0.4927, c=0.5423, Δ=7.46-0.5423, γ₀=1.79
|
||||
|
||||
Need to fit these for each (fc, q, sens) configuration group:
|
||||
t1kq: fc=800..1200, q=1.0, sens=12
|
||||
t1k: fc=500..2000, q=1.0, sens=12
|
||||
al: fc=1000, q=1.0, sens=3..24
|
||||
res: fc=300..700, q=1.0, sens=12
|
||||
dual: fc=500, q=0.1..10.0, sens=12
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
|
||||
sys.path.insert(0, '/home/m/re-tools/scripts')
|
||||
import corpus
|
||||
|
||||
corpus.RB = '/home/m/re-tools/dsp/build/render48k'
|
||||
|
||||
def structural_cases():
|
||||
out = []
|
||||
for name, inp, args, ref, f in corpus.build_cases():
|
||||
joined = [','.join(args)] if len(args) == 3 else args
|
||||
out.append((name, inp, joined, ref, f))
|
||||
return out
|
||||
|
||||
def group_key(name):
|
||||
return name.split('_')[0]
|
||||
|
||||
def load_ref_errors():
|
||||
"""Load baseline_bridge.json for target errors."""
|
||||
with open('scripts/baseline_bridge.json') as f:
|
||||
return json.load(f)
|
||||
|
||||
def render_vlaw(inp, out, args, alpha, beta, c, delta, gamma0, extra_env=None):
|
||||
"""Run render48k with VLAW parameters."""
|
||||
env = {
|
||||
**os.environ,
|
||||
'RT_VLAW': '1',
|
||||
'RT_VLAW_ALPHA': str(alpha),
|
||||
'RT_VLAW_BETA': str(beta),
|
||||
'RT_VLAW_C': str(c),
|
||||
'RT_VLAW_DELTA': str(delta),
|
||||
'RT_VLAW_GAMMA0': str(gamma0),
|
||||
'RT_SYN': '1',
|
||||
'RT_NOWARP': '1',
|
||||
'RT_NOIIR3': '1',
|
||||
'RT_IIR12': '0',
|
||||
}
|
||||
if extra_env:
|
||||
env.update(extra_env)
|
||||
subprocess.run(
|
||||
[corpus.RB, inp, out] + args,
|
||||
capture_output=True, text=True, env=env,
|
||||
cwd='/home/m/re-tools'
|
||||
)
|
||||
|
||||
def eval_config(alpha, beta, c, delta, gamma0, cases_subset=None):
|
||||
"""Evaluate VLAW params on cases, return per-group mean abs error."""
|
||||
all_cases = structural_cases()
|
||||
if cases_subset:
|
||||
all_cases = [c for c in all_cases if group_key(c[0]) in cases_subset]
|
||||
|
||||
refs = load_ref_errors()
|
||||
errs = {}
|
||||
|
||||
for name, inp, args, ref, f in all_cases:
|
||||
out = f'/tmp/vlaw_fit_{name}.wav'
|
||||
render_vlaw(inp, out, args, alpha, beta, c, delta, gamma0)
|
||||
|
||||
if not os.path.exists(out) or os.path.getsize(out) == 0:
|
||||
errs[name] = 999.0
|
||||
continue
|
||||
|
||||
try:
|
||||
ref_sig = corpus.load_mono(ref)
|
||||
out_sig = corpus.load_mono(out)
|
||||
min_len = min(len(ref_sig), len(out_sig))
|
||||
ref_sig = ref_sig[-min_len:]
|
||||
out_sig = out_sig[-min_len:]
|
||||
|
||||
ref_ta = corpus.ta(ref_sig, f)
|
||||
out_ta = corpus.ta(out_sig, f)
|
||||
err_db = corpus.db(out_ta / ref_ta)
|
||||
errs[name] = err_db
|
||||
except Exception as e:
|
||||
print(f"Error on {name}: {e}")
|
||||
errs[name] = 999.0
|
||||
|
||||
# Group stats
|
||||
groups = {}
|
||||
for k, v in errs.items():
|
||||
g = group_key(k)
|
||||
groups.setdefault(g, []).append(v)
|
||||
|
||||
out = {g: float(np.mean(np.abs(v))) for g, v in groups.items()}
|
||||
out['TOTAL'] = float(np.mean(np.abs(list(errs.values()))))
|
||||
return out, errs
|
||||
|
||||
def fit_alpha_beta_c(cases_to_fit):
|
||||
"""Coordinate descent on (α, β, c) for a specific case group."""
|
||||
# For now, grid search
|
||||
best = None
|
||||
best_err = float('inf')
|
||||
|
||||
# Search ranges around current dual(q=0.5) values
|
||||
for alpha in np.linspace(2.5, 4.0, 8):
|
||||
for beta in np.linspace(0.3, 0.7, 8):
|
||||
for c in np.linspace(0.2, 1.0, 8):
|
||||
stats, _ = eval_config(alpha, beta, c, 6.9, 1.79, cases_to_fit)
|
||||
total = stats['TOTAL']
|
||||
if total < best_err:
|
||||
best_err = total
|
||||
best = (alpha, beta, c, stats)
|
||||
print(f" New best: α={alpha:.4f}, β={beta:.4f}, c={c:.4f}, TOTAL={total:.4f}")
|
||||
|
||||
return best
|
||||
|
||||
def main():
|
||||
# Build case map by group
|
||||
all_cases = structural_cases()
|
||||
groups = {}
|
||||
for name, inp, args, ref, f in all_cases:
|
||||
g = group_key(name)
|
||||
groups.setdefault(g, []).append(name)
|
||||
|
||||
print("Available groups:", list(groups.keys()))
|
||||
for g, names in groups.items():
|
||||
print(f" {g}: {len(names)} cases")
|
||||
|
||||
# Start with dual group (already calibrated)
|
||||
print("\n=== Testing dual(q=0.5) baseline ===")
|
||||
stats, errs = eval_config(3.2193, 0.4927, 0.5423, 6.9177, 1.79, ['dual'])
|
||||
print(f"Dual stats: {stats}")
|
||||
|
||||
# Now fit for each group
|
||||
for g in ['t1kq', 't1k', 'al', 'res', 'dual']:
|
||||
if g not in groups:
|
||||
continue
|
||||
print(f"\n=== Fitting {g} ===")
|
||||
best = fit_alpha_beta_c([g])
|
||||
if best:
|
||||
alpha, beta, c, stats = best
|
||||
print(f" {g} best: α={alpha:.4f}, β={beta:.4f}, c={c:.4f}")
|
||||
print(f" Stats: {stats}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,113 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Quick VLAW parameter grid search - test fewer combos per case.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import json
|
||||
|
||||
sys.path.insert(0, '/home/m/re-tools/scripts')
|
||||
import corpus
|
||||
|
||||
corpus.RB = '/home/m/re-tools/dsp/build/render48k'
|
||||
|
||||
with open('scripts/baseline_bridge.json') as f:
|
||||
REF_ERRORS = json.load(f)
|
||||
|
||||
def structural_cases():
|
||||
out = []
|
||||
for name, inp, args, ref, f in corpus.build_cases():
|
||||
joined = [','.join(args)] if len(args) == 3 else args
|
||||
out.append((name, inp, joined, ref, f))
|
||||
return out
|
||||
|
||||
def run_vlaw(inp, args, alpha, beta, c, delta):
|
||||
out = f'/tmp/vlaw_{alpha}_{beta}_{c}_{delta}_{os.path.basename(inp)}.wav'
|
||||
env = {
|
||||
**os.environ,
|
||||
'RT_VLAW': '1',
|
||||
'RT_VLAW_ALPHA': str(alpha),
|
||||
'RT_VLAW_BETA': str(beta),
|
||||
'RT_VLAW_C': str(c),
|
||||
'RT_VLAW_DELTA': str(delta),
|
||||
'RT_SYN': '1', 'RT_NOWARP': '1', 'RT_NOIIR3': '1', 'RT_IIR12': '0',
|
||||
}
|
||||
subprocess.run([corpus.RB, inp, out] + args, capture_output=True, env=env, cwd='/home/m/re-tools')
|
||||
return out
|
||||
|
||||
def eval_error(out, ref, f):
|
||||
if not os.path.exists(out) or os.path.getsize(out) == 0:
|
||||
return None
|
||||
ref_sig = corpus.load_mono(ref)
|
||||
out_sig = corpus.load_mono(out)
|
||||
min_len = min(len(ref_sig), len(out_sig))
|
||||
ref_sig = ref_sig[-min_len:]
|
||||
out_sig = out_sig[-min_len:]
|
||||
ref_ta = corpus.ta(ref_sig, f)
|
||||
out_ta = corpus.ta(out_sig, f)
|
||||
return corpus.db(out_ta / ref_ta)
|
||||
|
||||
def group_key(name):
|
||||
return name.split('_')[0]
|
||||
|
||||
all_cases = structural_cases()
|
||||
groups = {}
|
||||
for name, inp, args, ref, f in all_cases:
|
||||
g = group_key(name)
|
||||
groups.setdefault(g, []).append((name, inp, args, ref, f))
|
||||
|
||||
# Pick one case per group
|
||||
rep_cases = {}
|
||||
for g in ['t1kq', 't1k', 'al', 'res', 'dual']:
|
||||
if g in groups:
|
||||
# Pick middle-ish case
|
||||
cases = groups[g]
|
||||
rep_cases[g] = cases[len(cases)//2]
|
||||
|
||||
print("Representative cases:")
|
||||
for g, (name, inp, args, ref, f) in rep_cases.items():
|
||||
print(f" {g}: {name}")
|
||||
|
||||
# Test a small grid around dual params
|
||||
dual_params = (3.2193, 0.4927, 0.5423, 6.9177)
|
||||
|
||||
print("\n=== Grid search per group ===")
|
||||
results = {}
|
||||
|
||||
for g, (name, inp, args, ref, f) in rep_cases.items():
|
||||
print(f"\n--- {g} ({name}) ---")
|
||||
best = None
|
||||
best_err = float('inf')
|
||||
|
||||
# Coarse grid
|
||||
alphas = np.linspace(1.0, 5.0, 5)
|
||||
betas = np.linspace(0.2, 0.8, 5)
|
||||
cs = np.linspace(-0.5, 2.0, 5)
|
||||
deltas = np.linspace(0.0, 12.0, 5)
|
||||
|
||||
for alpha in alphas:
|
||||
for beta in betas:
|
||||
for c in cs:
|
||||
for delta in deltas:
|
||||
out = run_vlaw(inp, args, alpha, beta, c, delta)
|
||||
err = eval_error(out, ref, f)
|
||||
if err is not None and abs(err) < best_err:
|
||||
best_err = abs(err)
|
||||
best = (alpha, beta, c, delta, err)
|
||||
print(f' {name}: α={alpha:.3f}, β={beta:.3f}, c={c:.3f}, Δ={delta:.3f} => {err:.3f} dB')
|
||||
|
||||
if best:
|
||||
results[g] = best[:4]
|
||||
print(f' BEST {g}: α={best[0]:.4f}, β={best[1]:.4f}, c={best[2]:.4f}, Δ={best[3]:.4f} => {best[4]:.3f} dB')
|
||||
|
||||
print("\n=== SUMMARY ===")
|
||||
for g, (a, b, c, d) in results.items():
|
||||
print(f'{g}: α={a:.4f}, β={b:.4f}, c={c:.4f}, Δ={d:.4f}')
|
||||
|
||||
with open('/tmp/opencode/vlaw_params.json', 'w') as f:
|
||||
json.dump({g: {'alpha': a, 'beta': b, 'c': c, 'delta': d}
|
||||
for g, (a, b, c, d) in results.items()}, f, indent=2)
|
||||
print('\nSaved to /tmp/opencode/vlaw_params.json')
|
||||
@@ -0,0 +1,71 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Quick VLAW parameter test - just evaluate a few configs per group.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
|
||||
sys.path.insert(0, '/home/m/re-tools/scripts')
|
||||
import corpus
|
||||
|
||||
corpus.RB = '/home/m/re-tools/dsp/build/render48k'
|
||||
|
||||
def run_one(inp, args, alpha, beta, c, delta, gamma0=1.79):
|
||||
out = f'/tmp/vlaw_test_{alpha}_{beta}_{c}.wav'
|
||||
env = {
|
||||
**os.environ,
|
||||
'RT_VLAW': '1',
|
||||
'RT_VLAW_ALPHA': str(alpha),
|
||||
'RT_VLAW_BETA': str(beta),
|
||||
'RT_VLAW_C': str(c),
|
||||
'RT_VLAW_DELTA': str(delta),
|
||||
'RT_SYN': '1',
|
||||
'RT_NOWARP': '1',
|
||||
'RT_NOIIR3': '1',
|
||||
'RT_IIR12': '0',
|
||||
}
|
||||
subprocess.run(
|
||||
[corpus.RB, inp, out] + args,
|
||||
capture_output=True, text=True, env=env,
|
||||
cwd='/home/m/re-tools'
|
||||
)
|
||||
return out
|
||||
|
||||
def eval_one(name, inp, args, ref, f, alpha, beta, c, delta):
|
||||
out = run_one(inp, args, alpha, beta, c, delta)
|
||||
if not os.path.exists(out) or os.path.getsize(out) == 0:
|
||||
return None
|
||||
ref_sig = corpus.load_mono(ref)
|
||||
out_sig = corpus.load_mono(out)
|
||||
min_len = min(len(ref_sig), len(out_sig))
|
||||
ref_sig = ref_sig[-min_len:]
|
||||
out_sig = out_sig[-min_len:]
|
||||
ref_ta = corpus.ta(ref_sig, f)
|
||||
out_ta = corpus.ta(out_sig, f)
|
||||
return corpus.db(out_ta / ref_ta)
|
||||
|
||||
# Test current VLAW params on different groups
|
||||
test_params = (3.2193, 0.4927, 0.5423, 6.9177)
|
||||
|
||||
all_cases = []
|
||||
for name, inp, args, ref, f in corpus.build_cases():
|
||||
joined = [','.join(args)] if len(args) == 3 else args
|
||||
all_cases.append((name, inp, joined, ref, f))
|
||||
|
||||
# Pick one representative case per group
|
||||
groups = {}
|
||||
for name, inp, args, ref, f in all_cases:
|
||||
g = name.split('_')[0]
|
||||
if g not in groups:
|
||||
groups[g] = (name, inp, args, ref, f)
|
||||
|
||||
print("Testing VLAW params (3.2193, 0.4927, 0.5423, 6.9177) on each group:")
|
||||
for g, (name, inp, args, ref, f) in groups.items():
|
||||
err = eval_one(name, inp, args, ref, f, *test_params)
|
||||
if err is not None:
|
||||
print(f" {name} ({g}): {err:.3f} dB")
|
||||
else:
|
||||
print(f" {name} ({g}): FAILED")
|
||||
Reference in New Issue
Block a user