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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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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 start of a musical event.
For more reading, see this tutorial on onset detection by Juan Bello.
Load the audio file simpleLoop.wav into the NumPy array x and sampling rate fs.
Step1:  Plot the signal
Step2:  Listen
Step3:  librosa.onset.onset_detect
librosa.onset.onset_detect returns the frame indices for estimated onsets in a signal
Step4:  Plot the onsets on top of a spectrogram of the audio
Step5:  essentia.standard.OnsetRate
The easiest way in Essentia to detect onsets given a time-domain signal is using OnsetRate. It returns a list of onset times and the onset rate, i.e. number of onsets per second.
Step6:  essentia.standard.AudioOnsetsMarker
To verify our results, we can use AudioOnsetsMarker to add a sound at the moment of each onset.
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Python Code:
x, fs = librosa.load('simpleLoop.wav', sr=44100)
print x.shape
Explanation: &larr; Back to Index
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 start of a musical event.
For more reading, see this tutorial on onset detection by Juan Bello.
Load the audio file simpleLoop.wav into the NumPy array x and sampling rate fs.
End of explanation
librosa.display.waveplot(x, fs)
Explanation: Plot the signal:
End of explanation
from IPython.display import Audio
Audio(x, rate=fs)
Explanation: Listen:
End of explanation
onsets = librosa.onset.onset_detect(x, fs)
print onsets # frame numbers of estimated onsets
Explanation: librosa.onset.onset_detect
librosa.onset.onset_detect returns the frame indices for estimated onsets in a signal:
End of explanation
S = librosa.stft(x)
logS = librosa.logamplitude(S)
librosa.display.specshow(logS, fs, alpha=0.75, x_axis='time')
plt.vlines(onsets, 0, logS.shape[0], color='r')
Explanation: Plot the onsets on top of a spectrogram of the audio:
End of explanation
from essentia.standard import OnsetRate
find_onsets = OnsetRate()
onset_times, onset_rate = find_onsets(x)
print onset_times
print onset_rate
Explanation: essentia.standard.OnsetRate
The easiest way in Essentia to detect onsets given a time-domain signal is using OnsetRate. It returns a list of onset times and the onset rate, i.e. number of onsets per second.
End of explanation
from essentia.standard import AudioOnsetsMarker
onsets_marker = AudioOnsetsMarker(onsets=onset_times, type='beep')
x_beeps = onsets_marker(x)
Audio(x_beeps, rate=fs)
Explanation: essentia.standard.AudioOnsetsMarker
To verify our results, we can use AudioOnsetsMarker to add a sound at the moment of each onset.
End of explanation
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