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# inline plot
%matplotlib inline

# displaying output
import matplotlib.pyplot as plt

# audio library
import librosa

# display audio via IPython
import IPython.display

# numerical libaray
import numpy as np


    

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# load the example track
y, sr = librosa.load(librosa.util.example_audio_file(), duration = 10.0)


    

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# play back
IPython.display.Audio(data=y, rate=sr)


    

    
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o_env = librosa.onset.onset_strength(y, sr=sr)
onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr)


    

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D = np.abs(librosa.stft(y))**2
plt.figure()
plt.subplot(2, 1, 1)
librosa.display.specshow(librosa.logamplitude(D, ref_power=np.max),x_axis='time', y_axis='log')
plt.title('Power spectrogram')
plt.subplot(2, 1, 2)
plt.plot(o_env, label='Onset strength')
plt.vlines(onset_frames, 0, o_env.max(), color='r', alpha=0.9, linestyle='--', label='Onsets')
plt.xticks([])
plt.axis('tight')
plt.legend(frameon=True, framealpha=0.75)
plt.show()


    

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