feat: restrict B in BassAnalyzer to sensible freq range

rhythm.py

@@ -13,6 +13,7 @@
 
 import numpy as np
 from numpy.fft import fft
+import matplotlib.pyplot as plt  # for debug only
 
 from hsh_signal.signal import lowpass_fft
 import time
@@ -81,7 +82,30 @@ def gabor_wavelet(omega, nu, fs, T, tt=None):
     psi = 1.0 / np.sqrt(omega) * np.exp(-np.pi * (t / omega)**2) * np.exp(1j*2*np.pi * nu * t / omega)
     return psi
 
-class BassAnalyzer:
+class Analyzer:
+    def __init__(self): pass
+    def debug_plot(self, i1, i2):
+        Scp2, path = self.Scp2, self.path
+        fs, Dp = self.fs, self.Dp
+
+        ss, omega, nu, fsp, Wp, I, J, freqs = self.pms
+
+        Scp2_slice = np.abs(Scp2[i1:i2])
+
+        plt.figure(figsize=(8,2))
+        plt.imshow(Scp2_slice.T, origin='lower')
+        x_positions = np.arange(Scp2_slice.shape[0]//250+1)*250
+        if x_positions[-1] == Scp2_slice.shape[0]:
+            x_positions[-1] -= 1  # so last tick is shown properly
+        t1 = i1 / (fs / Dp)
+        x_labels = ['{:.1f}'.format(t1+x*Dp/fs) for x in x_positions]
+        plt.xticks(x_positions, x_labels)
+        y_positions = np.arange(Scp2_slice.shape[1]//50)*50
+        y_labels = ['{:.1f}'.format((nu/(omega*ss[y]))) for y in y_positions]  # Hz equivalents of wavelet scale
+        plt.yticks(y_positions, y_labels)
+        plt.plot(np.arange(Scp2_slice.shape[0]), path[i1:i2], c='r')
+
+class BassAnalyzer(Analyzer):
     """
     Rhythm analysis from songs.
     Provides a beat amplitude signal from the audio signal.
@@ -112,6 +136,7 @@ class BassAnalyzer:
         :param fs: sampling rate
         :param sig: audio signal normalized to [-1,1]
         """
+        super(BassAnalyzer, self).__init__()
         self.D = int(self.shift_sec * fs)  #: spectrogram step
         if self.Wp_force:
             self.Wp = self.Wp_force
@@ -151,6 +176,11 @@ class BassAnalyzer:
         t8 = time.time()
         ampl = self._viterbi_ampl(Scp2, path)
         t9 = time.time()
+
+        self.Scp2 = Scp2
+        self.path = path
+        self.pms = pms
+
         if not dbg_time:
             return ampl
         else:
@@ -192,6 +222,11 @@ class BassAnalyzer:
         pt_re = (np.diff(pt) == 1).astype(int)  # rising edge
         self.B = max(np.sum(pt_re), 1)  # total number of pulses in the 'pt' pulse train signal
 
+        # clip B, to force **reasonable** frequency range for wavelets
+        # (noise will otherwise cause many transitions -> high B -> bass falls below freq range -> algo fail)
+        B_min, B_max = 0.5 * M / (fs / self.D), 5.0 * M / (fs / self.D)
+        self.B = np.clip(self.B, a_min=B_min, a_max=B_max)
+
         # resample 'pt' (M) at these indices -> 'ptr' (L), like original 'f' (signal padded)
         squashed_idxs = np.floor(np.linspace(0, L-1, L) * (M/L)).astype(int)
         ptr = pt[squashed_idxs]