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%matplotlib inline
import seaborn
import numpy, scipy, matplotlib.pyplot as plt, IPython.display as ipd
import librosa, librosa.display
plt.rcParams['figure.figsize'] = (11, 5)
In audio processing, it is common to operate on one frame at a time using a constant frame size and hop size (i.e. increment). Frames are typically chosen to be 10 to 100 ms in duration.
Let's create an audio signal consisting of a pure tone that gradually gets louder. Then, we will segment the signal and compute the root mean square (RMS) energy for each frame.
First, set our parameters:
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T = 3.0 # duration in seconds
sr = 22050 # sampling rate in Hertz
amplitude = numpy.logspace(-3, 0, int(T*sr), endpoint=False, base=10.0) # time-varying amplitude
print amplitude.min(), amplitude.max() # starts at 110 Hz, ends at 880 Hz
Create the signal:
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t = numpy.linspace(0, T, int(T*sr), endpoint=False)
x = amplitude*numpy.sin(2*numpy.pi*440*t)
Listen to the signal:
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ipd.Audio(x, rate=sr)
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Plot the signal:
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librosa.display.waveplot(x, sr=sr)
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In Python, you can use a standard list comprehension to perform segmentation of a signal and compute RMSE at the same time.
Initialize segmentation parameters:
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frame_length = 1024
hop_length = 512
Define a helper function:
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def rmse(x):
return numpy.sqrt(numpy.mean(x**2))
Using a list comprehension, plot the RMSE for each frame on a log-y axis:
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plt.semilogy([rmse(x[i:i+frame_length])
for i in range(0, len(x), hop_length)])
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Given a signal, librosa.util.frame will produce a list of uniformly sized frames:
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frames = librosa.util.frame(x, frame_length=frame_length, hop_length=hop_length)
plt.semilogy([rmse(frame) for frame in frames.T])
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That being said, in librosa, manual segmentation of a signal is often unnecessary, because the feature extraction methods themselves do segmentation for you.