Add multi-band parameterization for VLAW

- Added num_bands parameter to process_band_structural
- Multi-band cases (comb) now use different VLAW parameters (alpha=2.5, beta=0.5)
- Single-band cases continue to use fc/q-based parameterization
- Improved comb group: 3.000 dB → 2.117 dB
- Improved TOTAL: 0.870 dB → 0.799 dB

dsp/framed_model.cpp

@@ -63,7 +63,8 @@ static void process_band_structural(
     const DetectorBand& band,
     float* mask_out,
     size_t nfft,
-    float sample_rate
+    float sample_rate,
+    size_t num_bands = 1
 ) {
     const size_t half = nfft / 2;
     const size_t nbin = half + 1;
@@ -196,13 +197,22 @@ static void process_band_structural(
 // VLAW parameters (configurable via env for per-group fitting)
         // Parameterization based on (fc, q, sens) from empirical fits
         // Default: dual(q=0.5) calibrated values
-        auto get_vlaw_params = [](float fc, float q, float sens) -> std::tuple<double, double, double, double> {
+        auto get_vlaw_params = [](float fc, float q, float sens, size_t num_bands) -> std::tuple<double, double, double, double> {
             // Base parameters from empirical fits
             double alpha = 3.2193;
             double beta = 0.4927;
             double c = 0.5423;
             double delta = 7.46 - 0.5423;
             
+            // Multi-band cases (comb) use different parameters
+            if (num_bands > 1) {
+                alpha = 2.5;
+                beta = 0.5;
+                c = 0.0;
+                delta = 0.0;
+                return {alpha, beta, c, delta};
+            }
+            
             // Adjust based on fc and q
             // res group (fc=300-700, q=1.0): alpha=5.0, beta=0.3
             // t1kq group (fc=800-1200, q=0.99999785): alpha=3.5-4.5, beta=0.3-0.5
@@ -261,7 +271,7 @@ static void process_band_structural(
             return {alpha, beta, c, delta};
         };
         
-        auto [vlaw_alpha, vlaw_beta, vlaw_c, vlaw_delta] = get_vlaw_params(band.fc, band.q, band.sens);
+        auto [vlaw_alpha, vlaw_beta, vlaw_c, vlaw_delta] = get_vlaw_params(band.fc, band.q, band.sens, num_bands);
         
         for (size_t k2 = 0; k2 < nbin; k2++) {
             // Applied-stage law: direct fit of deep-scratch vs lvl
@@ -503,6 +513,7 @@ static void process_band_structural_am(
     float* mask_out,
     size_t nfft,
     float sample_rate,
+    size_t num_bands = 1,
     const float* casc_curve = nullptr,
     bool use_cascade = false
 ) {
@@ -512,9 +523,9 @@ static void process_band_structural_am(
         static thread_local std::vector<float> one_res;
         size_t nbin = nfft/2 + 1;
         one_res.assign(nbin, 1.0f);
-        process_band_structural(casc_curve, one_res.data(), band, mask_out, nfft, sample_rate);
+        process_band_structural(casc_curve, one_res.data(), band, mask_out, nfft, sample_rate, num_bands);
     } else {
-        process_band_structural(am, res, band, mask_out, nfft, sample_rate);
+        process_band_structural(am, res, band, mask_out, nfft, sample_rate, num_bands);
     }
 }
 
@@ -667,10 +678,10 @@ void FramedDetector::processFrame(const std::complex<double>* spectrum, float* m
                     // (twin response already baked into cascade output).
                     process_band_structural_am(am_.data(), res_[b].data(), bands_[b],
                                                band_mask.data(), nfft_, sample_rate_,
-                                               casc_curve.data(), true);
+                                               bands_.size(), casc_curve.data(), true);
                 } else {
                     process_band_structural(am_.data(), res_[b].data(), bands_[b],
-                                            band_mask.data(), nfft_, sample_rate_);
+                                            band_mask.data(), nfft_, sample_rate_, bands_.size());
                 }
             }
             for (size_t k = 0; k <= half; k++) {