def execute_flac_convert():
"""
Cycles through test_data, converting all flac to wav
Script includes a utility remove spaces and problem
characters from file name
"""
files = [f for f in glob('*.flac')]
for af in files:
x = flacread(af)[0]
log.debug("Found a flac file: '%s'" % af)
n = switch_ext(strip_all(af), '.wav')
print ("Converting '%s' to: '%s'" % (af, n))
wavwrite(x, n, 44100)
def open(filename, sampleRate=44100):
"""
Open a file (WAV or MP3), return instance of this class with data loaded in
Note that this is a static method. This is the preferred method of constructing this object
"""
filename = filename.lower()
if filename.endswith('mp3') or filename.endswith('m4a'):
ffmpeg = Popen([
"ffmpeg",
"-i", filename,
"-vn", "-acodec", "pcm_s16le", # Little Endian 16 bit PCM
"-ac", "1", "-ar", str(sampleRate), # -ac = audio channels (1)
"-f", "s16le", "-"], # -f wav for WAV file
stdin = PIPE, stdout = PIPE, stderr = open(os.devnull, "w"))
rawData = ffmpeg.stdout
mp3Array = numpy.fromstring(rawData.read(), numpy.int16)
mp3Array = mp3Array.astype('float32') / 32767.0
audioFile = mp3Array.view(AudioFile)
audioFile.sampleRate = sampleRate
audioFile.channels = 1
audioFile.format = pyaudio.paFloat32
return audioFile
elif filename.endswith('wav') or filename.endswith('flac'):
if filename.endswith('wav'):
sampleRate, samples = scipy.io.wavfile.read(filename)
if filename.endswith('flac'):
samples, sampleRate, encoding = flacread(filename)
# Convert to float
samples = samples.astype('float32') / 32767.0
# Get left channel
if len(samples.shape) > 1:
samples = samples[:, 0]
audioFile = samples.view(AudioFile)
audioFile.sampleRate = sampleRate
audioFile.channels = 1
audioFile.format = pyaudio.paFloat32
return audioFile
# coding : utf-8
# create by ztypl on 2017/9/7
from mdp import fastica
from scikits.audiolab import flacread, flacwrite
from numpy import array
sig1, fs1, enc1 = flacread('file1.flac')
sig2, fs2, enc2 = flacread('file2.flac')
sig1 = sig1[:, 0]
sig2 = sig2[:, 0]
minlen = min(len(sig1), len(sig2))
sig1 = sig1[:minlen]
sig2 = sig2[:minlen]
mixed1 = sig1 + 0.5 * sig2
mixed2 = sig1 + 0.8 * sig2
mixed = array([mixed1, mixed2]).T
flacwrite(mixed, 'Mixed2.flac')
if len(sys.argv) == 2:
inFile = sys.argv[1]
else:
if len(sys.argv) > 2:
quit("only one optional argument: path to wav file that is to be analyzed")
else:
# inFile = 'audio-samples/b112.d54.t481481.flac'
# inFile = 'audio-samples/b112.d92.t5639583333.flac'
# inFile = 'audio-samples/b1136.d94.t282148.flac'
# inFile = 'audio-samples/b1136.d94.t24981322.flac'
# inFile = 'audio-samples/b1233.d48.t4839703.flac'
# inFile = 'audio-samples/b1233.d92.t44511791.flac'
# inFile = 'audio-samples/b1592.d54.t66047281.flac'
inFile = 'audio-samples/b1592.d91.t65217093.flac'
data, sample_freq, encoding = flacread(inFile)
timeArray = np.arange(0.0, len(data), 1)
timeArray = timeArray / sample_freq
timeArray = timeArray * 1000 # scale to milliseconds
NFFT = 1000 # the length of the windowing segments
fft = fft(data) # FFT
bp = fft[:] # copy for manipulating //currently not needed
ibp = ifft(bp) # inverse FFT for spectrogram
ax1 = plt.subplot(4, 1, 1)
plt.plot(timeArray, data)
plt.subplot(4, 1, 2)
Pxx, freqs, bins, im = plt.specgram(data, Fs=sample_freq)
"""
# Compute Fourier transform of windowed signal
windowed = sig * blackmanharris(len(sig))
f = rfft(windowed)
# Find the peak and interpolate to get a more accurate peak
i = argmax(abs(f)) # Just use this for less-accurate, naive version
true_i = parabolic(log(abs(f)), i)[0]
# Convert to equivalent frequency
return fs * true_i / len(windowed)
filename = '440.wav'
print('Reading file "%s"\n' % filename)
try:
signal, fs = sf.read(filename)
except NameError:
signal, fs, enc = flacread(filename)
print('Calculating frequency from FFT:'),
start_time = time()
print('%f Hz' % freq_from_fft(signal, fs))
print('Time elapsed: %.3f s\n' % (time() - start_time))
print('Calculating frequency from zero crossings:'),
start_time = time()
print('%f Hz' % freq_from_crossings(signal, fs))
print('Time elapsed: %.3f s\n' % (time() - start_time))
for x in range(2,maxharms):
a = copy(c[::x]) #Should average or maximum instead of decimating
# max(c[::x],c[1::x],c[2::x],...)
c = c[:len(a)]
i = argmax(abs(c))
true_i = parabolic(abs(c), i)[0]
print 'Pass %d: %f Hz' % (x, fs * true_i / len(windowed))
c *= a
subplot(maxharms,1,x)
plot(log(c))
show()
filename ='/angry/1.wav'
print 'Reading file "%s"\n' % filename
signal, fs, enc = flacread(filename)
print 'Calculating frequency from FFT:',
start_time = time()
print '%f Hz' % freq_from_fft(signal, fs)
print 'Time elapsed: %.3f s\n' % (time() - start_time)
print 'Calculating frequency from zero crossings:',
start_time = time()
print '%f Hz' % freq_from_crossings(signal, fs)
print 'Time elapsed: %.3f s\n' % (time() - start_time)
print 'Calculating frequency from autocorrelation:',
start_time = time()
print '%f Hz' % freq_from_autocorr(signal, fs)
print 'Time elapsed: %.3f s\n' % (time() - start_time)
# coding : utf-8
# create by ztypl on 2017/9/7
from mdp import fastica
from scikits.audiolab import flacread, flacwrite
from numpy import abs, max
# Load in the stereo file
recording, fs, enc = flacread('Mixed3.flac')
# Perform FastICA algorithm on the two channels
sources = fastica(recording)
# The output levels of this algorithm are arbitrary, so normalize them to 1.0.
sources /= max(abs(sources), axis=0)
source1 = sources[:, 0]
source2 = sources[:, 1]
# Write back to a file
flacwrite(source1, 'source21.flac', fs, enc)
flacwrite(source2, 'source22.flac', fs, enc)
from scikits.audiolab import flacread, play
from scipy.signal import remez, lfilter
#from pylab import *
import matplotlib.pyplot as plt
import numpy as np
# convert mp3, read wav
#mp3filename = 'XC124158.mp3'
flacfile = '/home/tdh5188/workspace/comp596/Convnet-Music-Classification/03-Time.flac'
#wname = mktemp('.wav')
#print(wname)
#check_call(['avconv', '-i', flacfile, wname])
#sig, fs, enc = flacread(wname)
sig, fs, enc = flacread(flacfile)
#os.unlink(wname)
# bandpass filter
bands = np.array([0, 3500, 4000, 5500, 6000, fs / 2.0]) / fs
desired = [0, 1, 0]
b = remez(513, bands, desired)
sig_filt = lfilter(b, 1, sig)
sig_filt /= 1.05 * max(abs(sig_filt)) # normalize
subplot(211)
specgram(sig, Fs=fs, NFFT=1024, noverlap=0)
axis('tight')
axis(ymax=8000)
title('Original')
print fft
w = np.fft.fft(data)
ws = w # [:(len(w)/2)]
freqs = np.fft.fftfreq(len(ws))
print 'koperek!'
print(freqs.min(),freqs.max())
# (-0.5, 0.499975)
# Find the peak in the coefficients
idx=np.argmax(np.abs(ws)**2)
freq=freqs[idx]
freq_in_hertz=abs(freq*sample_frequency)
print 'frequency new: %f' % freq_in_hertz
# 439.8975
signal, fs, enc = flacread("l2.flac")
print 'frequency autocorr: %f' % freq_from_autocorr(signal,fs)
fft = scipy.fft(data)
print len(fft)
subplot(222)
plot(t,data)
subplot(223)
plot(t,fft)
subplot(224)
Pxx, freqs, bins, im = specgram(data, Fs=sample_frequency, NFFT=1024,
noverlap=900, cmap=cm.gist_heat)
show()
