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 analyzeSong(filename):
# generate the average fourier transform of a kick
snare = numpy.genfromtxt('snaredata.csv', delimiter=",")
kick = numpy.genfromtxt('kickdata.csv', delimiter=",")
meanSnare, covar = analyzeSoundType(snare)
meanKick, covar = analyzeSoundType(kick)
dataMat = readSong(filename)
matrix = numpy.array([])
for i in range(0, len(dataMat), 5):
dist = numpy.zeros(len(dataMat))
for j in range(0, 4):
dist[j] = numpy.dot(meanVec, dataMat[i + j])
minDistIndex = numpy.argmin(dist)
matrix = build_data.addToFeatureMatrix(matrix, dataMat[i + minDistIndex])
build_data.saveFile(filename + '.csv', 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)