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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Segmentation
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
Step1:  Create the signal
Step2:  Listen to the signal
Step3:  Plot the signal
Step4:  Segmentation Using Python List Comprehensions
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
Step5:  Define a helper function
Step6:  Using a list comprehension, plot the RMSE for each frame on a log-y axis
Step7:  librosa.util.frame
Given a signal, librosa.util.frame will produce a list of uniformly sized frames
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Python Code:
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
Explanation: &larr; Back to Index
Segmentation
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:
End of explanation
t = numpy.linspace(0, T, int(T*sr), endpoint=False)
x = amplitude*numpy.sin(2*numpy.pi*440*t)
Explanation: Create the signal:
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ipd.Audio(x, rate=sr)
Explanation: Listen to the signal:
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librosa.display.waveplot(x, sr=sr)
Explanation: Plot the signal:
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frame_length = 1024
hop_length = 512
Explanation: Segmentation Using Python List Comprehensions
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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def rmse(x):
    return numpy.sqrt(numpy.mean(x**2))
Explanation: Define a helper function:
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plt.semilogy([rmse(x[i:i+frame_length])
              for i in range(0, len(x), hop_length)])
Explanation: Using a list comprehension, plot the RMSE for each frame on a log-y axis:
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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])
Explanation: librosa.util.frame
Given a signal, librosa.util.frame will produce a list of uniformly sized frames:
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