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Showing posts with label LibROSA. Show all posts
Showing posts with label LibROSA. Show all posts

Thursday, December 27, 2018

Using LibROSA python module.

This python module named LibROSA is a python package for music and audio analysis and provides the building blocks necessary to create music information retrieval systems.
C:\Python364>cd Scripts
C:\Python364\Scripts>pip install librosa
Collecting librosa
...
Successfully installed audioread-2.1.6 joblib-0.13.0 librosa-0.6.2 llvmlite-0.26.0 numba-0.41.0 resampy-0.2.1 
scikit-learn-0.20.2
Let's create one waveform and a spectrogram with this python module.
The waveform (for sound) the term describes a depiction of the pattern of sound pressure variation (or amplitude) in the time domain.
A spectrogram (known also like sonographs, voiceprints, or voicegrams) is a visual representation of the spectrum of frequencies of sound or other signals as they vary with time.
I used a free WAV file sound from here.
The result of the waveform and spectrogram for that audio file is shown into next screenshots:


My example show first the waveform and you need to close the it to see the spectrogram.
Let's see the source code of this example:
import librosa
import librosa.display
import matplotlib.pyplot as plt
plt.figure(figsize=(14, 5))
path = "merry_christmas.wav"
out,samples = librosa.load(path)
print(out.shape, samples)
librosa.display.waveplot(out, sr=samples)
plt.show()
stft_array = librosa.stft(out)
stft_array_db = librosa.amplitude_to_db(abs(stft_array))
librosa.display.specshow(stft_array_db,sr=samples,x_axis='time', y_axis='hz')
plt.colorbar()
plt.show()