MFCC

MFCC/sigproc.py

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+import decimal
+import numpy
+import math
+import logging
+
+
+def round_half_up(number):
+    return int(decimal.Decimal(number).quantize(decimal.Decimal('1'), rounding=decimal.ROUND_HALF_UP))
+
+
+def rolling_window(a, window, step=1):
+    # http://ellisvalentiner.com/post/2017-03-21-np-strides-trick
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return numpy.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)[::step]
+
+
+def framesig(sig, frame_len, frame_step, winfunc=lambda x: numpy.ones((x,)), stride_trick=True):
+    """Frame a signal into overlapping frames.
+
+    :param sig: the audio signal to frame.
+    :param frame_len: length of each frame measured in samples.
+    :param frame_step: number of samples after the start of the previous frame that the next frame should begin.
+    :param winfunc: the analysis window to apply to each frame. By default no window is applied.
+    :param stride_trick: use stride trick to compute the rolling window and window multiplication faster
+    :returns: an array of frames. Size is NUMFRAMES by frame_len.
+    """
+    slen = len(sig)
+    frame_len = int(round_half_up(frame_len))
+    frame_step = int(round_half_up(frame_step))
+    if slen <= frame_len:
+        numframes = 1
+    else:
+        numframes = 1 + int(math.ceil((1.0 * slen - frame_len) / frame_step))
+
+    padlen = int((numframes - 1) * frame_step + frame_len)
+
+    zeros = numpy.zeros((padlen - slen,))
+    padsignal = numpy.concatenate((sig, zeros))
+    if stride_trick:
+        win = winfunc(frame_len)
+        frames = rolling_window(padsignal, window=frame_len, step=frame_step)
+    else:
+        indices = numpy.tile(numpy.arange(0, frame_len), (numframes, 1)) + numpy.tile(
+            numpy.arange(0, numframes * frame_step, frame_step), (frame_len, 1)).T
+        indices = numpy.array(indices, dtype=numpy.int32)
+        frames = padsignal[indices]
+        win = numpy.tile(winfunc(frame_len), (numframes, 1))
+
+    return frames * win
+
+
+def deframesig(frames, siglen, frame_len, frame_step, winfunc=lambda x: numpy.ones((x,))):
+    """Does overlap-add procedure to undo the action of framesig.
+
+    :param frames: the array of frames.
+    :param siglen: the length of the desired signal, use 0 if unknown. Output will be truncated to siglen samples.
+    :param frame_len: length of each frame measured in samples.
+    :param frame_step: number of samples after the start of the previous frame that the next frame should begin.
+    :param winfunc: the analysis window to apply to each frame. By default no window is applied.
+    :returns: a 1-D signal.
+    """
+    frame_len = round_half_up(frame_len)
+    frame_step = round_half_up(frame_step)
+    numframes = numpy.shape(frames)[0]
+    assert numpy.shape(frames)[1] == frame_len, '"frames" matrix is wrong size, 2nd dim is not equal to frame_len'
+
+    indices = numpy.tile(numpy.arange(0, frame_len), (numframes, 1)) + numpy.tile(
+        numpy.arange(0, numframes * frame_step, frame_step), (frame_len, 1)).T
+    indices = numpy.array(indices, dtype=numpy.int32)
+    padlen = (numframes - 1) * frame_step + frame_len
+
+    if siglen <= 0: siglen = padlen
+
+    rec_signal = numpy.zeros((padlen,))
+    window_correction = numpy.zeros((padlen,))
+    win = winfunc(frame_len)
+
+    for i in range(0, numframes):
+        window_correction[indices[i, :]] = window_correction[
+                                               indices[i, :]] + win + 1e-15  # add a little bit so it is never zero
+        rec_signal[indices[i, :]] = rec_signal[indices[i, :]] + frames[i, :]
+
+    rec_signal = rec_signal / window_correction
+    return rec_signal[0:siglen]
+
+
+def magspec(frames, NFFT):
+    """Compute the magnitude spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1).
+
+    :param frames: the array of frames. Each row is a frame.
+    :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded.
+    :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the magnitude spectrum of the corresponding frame.
+    """
+    if numpy.shape(frames)[1] > NFFT:
+        logging.warn(
+            'frame length (%d) is greater than FFT size (%d), frame will be truncated. Increase NFFT to avoid.',
+            numpy.shape(frames)[1], NFFT)
+    complex_spec = numpy.fft.rfft(frames, NFFT)
+    return numpy.absolute(complex_spec)
+
+
+def powspec(frames, NFFT):
+    """Compute the power spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1).
+
+    :param frames: the array of frames. Each row is a frame.
+    :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded.
+    :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the power spectrum of the corresponding frame.
+    """
+    return 1.0 / NFFT * numpy.square(magspec(frames, NFFT))
+
+
+def logpowspec(frames, NFFT, norm=1):
+    """Compute the log power spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1).
+
+    :param frames: the array of frames. Each row is a frame.
+    :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded.
+    :param norm: If norm=1, the log power spectrum is normalised so that the max value (across all frames) is 0.
+    :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the log power spectrum of the corresponding frame.
+    """
+    ps = powspec(frames, NFFT);
+    ps[ps <= 1e-30] = 1e-30
+    lps = 10 * numpy.log10(ps)
+    if norm:
+        return lps - numpy.max(lps)
+    else:
+        return lps
+
+
+def preemphasis(signal, coeff=0.95):
+    """perform preemphasis on the input signal.
+
+    :param signal: The signal to filter.
+    :param coeff: The preemphasis coefficient. 0 is no filter, default is 0.95.
+    :returns: the filtered signal.
+    """
+    return numpy.append(signal[0], signal[1:] - coeff * signal[:-1])