def get_ffts(audio_file):
"""Computes the FFT of each frame of a WAVE file.
Splits the WAVE file into frames of equal temporal length and performs
an FFT on each.
Args:
audio_file: A WAVE file.
Returns:
An iterable of the FFTs of the frames of the WAVE file.
"""
global COMP_FRAME_SIZE
# Read the file, and determine its length in frames
(sample, data) = utils.read_wave_from_file(audio_file)
total_frames = (data.size / sample) / COMP_FRAME_SIZE
# Allocate space for the FFT decompsitions of each frame of sound data
fft_out = numpy.ndarray(shape=(total_frames, sample * COMP_FRAME_SIZE), dtype=numpy.complex128)
# Loop invariant:
# 0 <= frame_index <= total_frames
# results in an array (fft_out) of FFTs that correspond to the frames of
# the audio file
frame_index = 0
while frame_index < total_frames:
fft = numpy.fft.fft(data[frame_index * COMP_FRAME_SIZE * sample : (frame_index + 1) * COMP_FRAME_SIZE * sample])
fft_out[frame_index] = fft
frame_index = frame_index + 1
return fft_out
def get_mfcc(path):
"""Finds the MFCCs and FFTs of a WAVE file.
Args:
path: The path to a WAVE file.
Returns:
A tuple of two iterables, the FFTs and MFCCs of the frames of the
WAVE file.
"""
global COMP_FRAME_SIZE
# Read the file, and determine its length in frames
(sample, data) = utils.read_wave_from_file(path)
total_frames = (data.size / sample) / COMP_FRAME_SIZE
step = COMP_FRAME_SIZE * sample
window = hamming(step)
# Allocate space for the FFT decompositions of each frame of sound data
fft_out = []
mfcc_out = []
# Loop invariant:
# 0 <= frame_index <= total_frames
# results in an array (fft_out) of FFTs that correspond to the
# frames of the WAVE file
filterbank_cache = {}
frame_index = 0
while frame_index + (1 - FRAME_OVERLAP_FACTOR) < total_frames:
# Obtain the frame_indexth frame from the data
frame = data[frame_index * step : (frame_index + 1) * step]
# Generate the FFT of the frame windowed by the hamming window
frame_fft = numpy.fft.rfft(frame * window, n=256)
frame_fft[frame_fft == 0] = 0.000003
nfft = len(frame_fft)
# Compute the mel triangular filterbank or get a cached version
fb_key = (sample, nfft)
if fb_key in filterbank_cache:
filterbank = filterbank_cache[fb_key]
else:
filterbank = triangular_filters(sample, nfft).T
filterbank[filterbank == 0] = 0.00003
filterbank_cache[fb_key] = filterbank
# The power spectrum of the frame
power_spectrum = numpy.abs(frame_fft)
# Filtered by the mel filterbank
mel_power_spectrum = numpy.log10(numpy.dot(power_spectrum, filterbank))
# With the discrete cosine transform to find the cepstrum
cepstrum = dct(mel_power_spectrum, type=2, norm="ortho", axis=-1)
fft_out.append(frame_fft)
mfcc_out.append(cepstrum[: int(len(cepstrum) * SIGNIFICANT_MFCC)])
frame_index = frame_index + FRAME_OVERLAP_FACTOR
return numpy.array(mfcc_out)
