Add scripts to preprocess audio signal

Signed-off-by: Adam Wawrzynski <[email protected]>

modules/audio_preprocessing.py

@@ -0,0 +1,92 @@
+import numpy as np
+import scipy.io.wavfile
+from scipy.fftpack import dct
+import matplotlib.pyplot as plt
+
+def emphesize_signal(signal, pre_emphasis = 0.97):
+    ''' Filter signal to amplify the high frequencies '''
+    emphasized_signal = np.append(signal[0], signal[1:] - pre_emphasis * signal[:-1])
+    return emphasized_signal
+
+def get_filter_banks(signal, sample_rate, frame_size = 0.025, frame_stride = 0.01, NFFT = 512, nfilt = 40):
+    ''' Calculate filter banks from signal '''
+    emphasized_signal = emphesize_signal(signal)
+
+    # Convert from seconds to samples
+    frame_length, frame_step = frame_size * sample_rate, frame_stride * sample_rate
+    signal_length = len(emphasized_signal)
+    frame_length = int(round(frame_length))
+    frame_step = int(round(frame_step))
+
+    # Make sure that we have at least 1 frame
+    num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))
+
+    pad_signal_length = num_frames * frame_step + frame_length
+    z = np.zeros((pad_signal_length - signal_length))
+
+    # Pad Signal to make sure that all frames have equal number of samples 
+    # without truncating any samples from the original signal
+    pad_signal = np.append(emphasized_signal, z)
+
+    indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
+    frames = pad_signal[indices.astype(np.int32, copy=False)]
+
+    frames *= np.hamming(frame_length)
+    # frames *= 0.54 - 0.46 * np.cos((2 * np.pi * n) / (frame_length - 1))  # Explicit Implementation **
+
+    # Magnitude of the FFT
+    mag_frames = np.absolute(np.fft.rfft(frames, NFFT))
+
+    # Power Spectrum
+    pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2))
+
+    low_freq_mel = 0
+
+    # Convert Hz to Mel
+    high_freq_mel = (2595 * np.log10(1 + (sample_rate / 2) / 700))
+
+    #  Equally spaced in Mel scale
+    mel_points = np.linspace(low_freq_mel, high_freq_mel, nfilt + 2)
+
+    # Convert Mel to Hz
+    hz_points = (700 * (10**(mel_points / 2595) - 1))
+    bin = np.floor((NFFT + 1) * hz_points / sample_rate)
+
+    fbank = np.zeros((nfilt, int(np.floor(NFFT / 2 + 1))))
+    for m in range(1, nfilt + 1):
+        f_m_minus = int(bin[m - 1])   # left
+        f_m = int(bin[m])             # center
+        f_m_plus = int(bin[m + 1])    # right
+
+        for k in range(f_m_minus, f_m):
+            fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])
+        for k in range(f_m, f_m_plus):
+            fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])
+    filter_banks = np.dot(pow_frames, fbank.T)
+    filter_banks = np.where(filter_banks == 0, np.finfo(float).eps, filter_banks)  # Numerical Stability
+    filter_banks = 20 * np.log10(filter_banks)  # dB
+    return filter_banks
+
+def get_mfcc(filter_banks, num_ceps = 12):
+    ''' Calculate MFCC from filter banks '''
+    mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)]
+    return mfcc
+
+def normalize_mfcc(mfcc, filter_banks, cep_lifter = 100):
+    ''' Normalize MFCC '''
+    nframes, ncoeff = mfcc.shape
+    n = np.arange(ncoeff)
+    lift = 1 + (cep_lifter / 2) * np.sin(np.pi * n / cep_lifter)
+    mfcc *= lift
+    filter_banks -= (np.mean(filter_banks, axis=0)) # + 1e-8)
+    mfcc -= (np.mean(mfcc, axis=0)) # + 1e-8)
+    return mfcc
+
+def plot_spectrogram(spectrogram, title):
+    ''' Plot spectrogram '''
+    plt.figure(figsize=(12, 5))
+    plt.title(title)
+    plt.imshow(spectrogram.T, cmap=plt.cm.jet, aspect='auto')
+    plt.xticks(np.arange(0, (spectrogram.T).shape[1], int((spectrogram.T).shape[1] / 4)))
+    ax = plt.gca()
+    ax.invert_yaxis()