def createSnareFft():
# read in the snare drum sample
files = glob.glob('./snare_samples/*.wav')
matrix = numpy.array([])
for filename in files:
print filename
rate, data = wave_reader.readWav(filename)
data = wave_reader.stereoToMono(data)
features = analyzer.getFrequencies(data)
matrix = build_data.addToFeatureMatrix(matrix, features[0:2000])
build_data.saveFile('snaredata.csv', matrix)
def wavToFeatures(filename):
rate, data = wavfile.read(filename)
if data.ndim > 1:
data = stereoToMono(data)
segDiv = 4 #1/10 of a second
segSize = rate / segDiv
start = 0
end = segSize
matrix = numpy.array([])
for i in range(0, data.size / segSize):
data_seg = data[start:end]
start += segSize
end += segSize
features = analyzer.getFrequencies(data_seg)
matrix = build_data.addToFeatureMatrix(matrix, features[0:2000])
# build_data.saveFile('testdata.csv', matrix)
return matrix
def readSong(filename, segTime = 0.05): # read in the input song rate, data = wave_reader.readWav(filename) data = wave_reader.stereoToMono(data) # sample every 0.1 seconds by default segSize = segTime * rate matrix = numpy.array([]) print 'reading in song...' start = 0 end = segSize for i in range(0, int(math.floor(len(data)/segSize))): data_seg = data[start:end] start += segSize end += segSize features = analyzer.getFrequencies(data_seg) matrix = build_data.addToFeatureMatrix(matrix, features[0:2000]) outfile = os.path.splitext(filename)[0] + '.csv' build_data.saveFile(outfile, matrix) return matrix
def extractFeatures(track, div):
rate, data = wavfile.read(track + '.wav')
if data.ndim > 1:
data = stereoToMono(data)
matrix = numpy.array([])
vmatrix = numpy.array([])
segSize = rate / div
start = 0
end = segSize
for i in range(0, data.size / segSize):
data_seg = data[start:end]
start += segSize
end += segSize
features = analyzer.getFrequencies(data_seg)[0:5000]
matrix = build_data.addToFeatureMatrix(matrix, features)
vmatrix = build_data.addToFeatureMatrix(vmatrix,
numpy.array([numpy.argmax(features)]))
build_data.saveFile(track + '_seg.csv', matrix)
build_data.saveFile(track + '_volume.csv', vmatrix)
def saveFile(filename, data):
numpy.savetxt(filename, data, delimiter=',', fmt='%.2f', newline = '\n')
def addToFeatureMatrix(matrix, feature):
feature = feature.reshape(1, feature.shape[0])
if matrix.size is 0:
matrix = feature;
else:
matrix = numpy.vstack((matrix, feature))
return matrix
if __name__ == '__main__':
matrix = numpy.array([])
seconds = 1
for note in range(0, 12):
print "generating note " + str(note)
for x in range(0, 100):
notesToPlay = chord_gen.createMajorChords(note)
length = int(seconds * 44100)
sample = numpy.zeros(length)
for i in range(0, len(notesToPlay)):
freq = midi_util.midiToFrequency(notesToPlay[i])
sample += wave_gen.saw(freq, seconds, 44100)
features = analyzer.getFrequencies(sample)
features = numpy.append(features[0:5000], note)
#features = features.astype(numpy.int16)
matrix = addToFeatureMatrix(matrix, features)
saveFile('largedata.csv', matrix)
