%matplotlib inline
import seaborn
import numpy, scipy, matplotlib.pyplot as plt, pandas, librosa, IPython.display as ipd, urllib

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Onset Detection

Automatic detection of musical events in an audio signal is one of the most fundamental tasks in music information retrieval. Here, we will show how to detect an onset, the very instant that marks the beginning of the transient part of a sound, or the earliest moment at which a transient can be reliably detected.

For more reading, see this tutorial on onset detection by Juan Bello.

Download an audio file:

filename = 'Classic Rock Beat 06.wav'
urllib.urlretrieve(
    'http://audio.musicinformationretrieval.com/Jam Pack 1/' + filename,
    filename=filename
)

('Classic Rock Beat 06.wav', <httplib.HTTPMessage instance at 0x1155af950>)

Load the audio file into the NumPy array x and sampling rate fs.

x, fs = librosa.load(filename)
print x.shape, fs

(151521,) 22050

Plot the signal:

librosa.display.waveplot(x, fs)

<matplotlib.collections.PolyCollection at 0x115bacf90>

Listen:

ipd.Audio(x, rate=fs)

librosa.onset.onset_detect

librosa.onset.onset_detect returns the frame indices for estimated onsets in a signal:

onset_frames = librosa.onset.onset_detect(x, sr=fs)
print onset_frames # frame numbers of estimated onsets

[ 20  29  38  57  66  75  84  93 103 112 121 131 140 149 158 167 176 185
 196 204 213 232 241 250 260 269 278 288]

Plot the onsets on top of a spectrogram of the audio:

S = librosa.stft(x)
logS = librosa.logamplitude(S)
librosa.display.specshow(logS, fs, x_axis='time')
plt.vlines(onset_frames, 0, logS.shape[0], color='k', alpha=0.8)

<matplotlib.collections.LineCollection at 0x115dd6d50>

Let's also plot the onsets with the time-domain waveform. Convert onsets to units of seconds:

onset_times = librosa.frames_to_time(onset_frames)
print onset_times

[ 0.46439909  0.67337868  0.88235828  1.32353741  1.53251701  1.7414966
  1.95047619  2.15945578  2.39165533  2.60063492  2.80961451  3.04181406
  3.25079365  3.45977324  3.66875283  3.87773243  4.08671202  4.29569161
  4.55111111  4.73687075  4.94585034  5.38702948  5.59600907  5.80498866
  6.03718821  6.2461678   6.45514739  6.68734694]

Plot the waveform with the detected onsets:

librosa.display.waveplot(x, sr=fs)
plt.vlines(onset_times*1000, -0.8, 0.79, color='r', alpha=0.8) 
# waveplot uses 1000 samples per second for visualization

<matplotlib.collections.LineCollection at 0x11629e690>

librosa.clicks

We can add a click at the location of each detected onset.

clicks = librosa.clicks(frames=onset_frames, sr=fs, length=len(x))

Listen to the original audio plus the detected onsets:

ipd.Audio(x + clicks, rate=fs)

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