stuffs

python/features/sigproc.py

@@ -0,0 +1,113 @@
+# This file includes routines for basic signal processing including framing and computing power spectra.
+# Author: James Lyons 2012
+
+import numpy
+import math
+
+def framesig(sig,frame_len,frame_step,winfunc=lambda x:numpy.ones((1,x))):
+    """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.    
+    :returns: an array of frames. Size is NUMFRAMES by frame_len.
+    """
+    slen = len(sig)
+    frame_len = int(round(frame_len))
+    frame_step = int(round(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))
+    
+    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((1,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(frame_len)
+    frame_step = round(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((1,padlen))
+    window_correction = numpy.zeros((1,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 NxNFFT. 
+
+    :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 NxNFFT. Each row will be the magnitude spectrum of the corresponding frame.
+    """    
+    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 NxNFFT. 
+
+    :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 NxNFFT. 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 NxNFFT. 
+
+    :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 1.
+    :returns: If frames is an NxD matrix, output will be NxNFFT. 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])
+
+
+