plt.bar(y_pos, quantities, align='center')
plt.title("notes pulled from wave by quantity, naive")
plt.xlabel("Note names and octaves")
plt.ylabel("quantity")
plt.xticks(y_pos, objects)
plt.show()
if __name__ == '__main__':
global dt
# figure out the correct dt length based on the tempo of the output wave you want
dt = compute_dt(bpm)
# read and parse each sample wave
for i in range(1, len(sys.argv) - 2):
#read in the wave file and unpack its data
wave_data = waveIO.read_wav_file(sys.argv[i])
wave_data = waveIO.unpack(wave_data)
wave_time = len(wave_data) / sr
note_length = dt * sr
wave_chunks = split_wave(wave_data, int(note_length))
#test_sample_wave(wave_chunks)
for i in range(len(wave_chunks)):
chunk = wave_chunks[i]
if len(chunk) != int(note_length):
continue
store_note(chunk)
import numpy
import sys
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import waveIO
musicwave = waveIO.read_wav_file(sys.argv[1])
musicwave = waveIO.unpack(musicwave)
data, freqs, bins, im = plt.specgram(musicwave, NFFT=2048, Fs=44100, noverlap=900)
plt.ylim(0,800)
plt.title("Spectrogram of {!s}".format(sys.argv[1]))
plt.show()
import numpy
import sys
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import waveIO
sr = 44100.
wave_data = waveIO.read_wav_file('vocalscales.wav')
wave_data = waveIO.unpack(wave_data)
data, freqs, bins, im = plt.specgram(wave_data, NFFT=1024, Fs=sr, noverlap=900)
plt.ylim(0,2000)
plt.title("Spectrogram of vocalscales.wav")
plt.xlabel("Time (s)")
plt.ylabel("amplitude")
plt.show()
time = len(wave_data) / sr
time = numpy.linspace(0,time,len(wave_data))
plt.plot(time,wave_data)
plt.title("Time domain of vocalscales.wav")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.show()
plt.bar(y_pos, quantities, align='center', width=0.75)
plt.title("notes pulled from wave by quantity, stft")
plt.xlabel("Note names and octaves")
plt.ylabel("quantity")
plt.xticks(y_pos, objects, fontsize=10)
plt.show()
if __name__ == '__main__':
WINDOW_FUNC = window_hanning
# read and parse each input wave
for i in range(1, len(sys.argv) - 2):
#read in the wave file and unpack its data
wave_data = waveIO.read_wav_file(sys.argv[i])
wave_data = waveIO.unpack(wave_data)
wave_time = len(wave_data)/sr
wave_chunks = []
#Instead of multiplying the entire wave by the windowing function, we can simply examine window-sized pieces of the wave at a time
#counting by WINDOW_LENGTH*(1-OVERLAP_PERCENT) allows adjustment of what percent of each window overlaps with the next
#Example: OVERLAP_PERCENT = 0 means the next window will start where the previous window ended
#Example: OVERLAP_PERCENT = 0.5 means the next window will start at the halfway mark of the previous window
#This is necessary when using nonrectangular window functions, to prevent loss of data
for i in range(0, len(wave_data), int(WINDOW_LENGTH*(1-OVERLAP_PERCENT))):
window = []
window.extend(wave_data[i:i+WINDOW_LENGTH])
#multiply by the window function
import numpy
import sys
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import waveIO
sr = 44100.
wave_data = waveIO.read_wav_file('vocalscales.wav')
wave_data = waveIO.unpack(wave_data)
data, freqs, bins, im = plt.specgram(wave_data, NFFT=1024, Fs=sr, noverlap=900)
plt.ylim(0, 2000)
plt.title("Spectrogram of vocalscales.wav")
plt.xlabel("Time (s)")
plt.ylabel("amplitude")
plt.show()
time = len(wave_data) / sr
time = numpy.linspace(0, time, len(wave_data))
plt.plot(time, wave_data)
plt.title("Time domain of vocalscales.wav")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.show()