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audio_classification

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CNN 1D vs 2D audio classification

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# audio_classification

Description of the approach : https://medium.com/@CVxTz/audio-classification-a-convolutional-neural-network-approach-b0a4fce8f6c

Requirement : Keras, tensorflow, numpy, librosa

# Audio Classification : A Convolutional Neural Network Approach

Audio Classification can be used for audio scene understanding which in turn is
important so that an artificial agent is able to understand and better interact
with its environment.<br> This is the motivation for this blog post, I will
present two different ways that you can go about doing audio classification
based on convolutions.

We will base our experiments on the dataset available at
([https://www.kaggle.com/c/freesound-audio-tagging](https://www.kaggle.com/c/freesound-audio-tagging))
which is a data-set of annotated audio segments of different lengths and out of
41 classes like “Acoustic_guitar”, “Applause”, “Bark” …

First Approach : Raw audio wave and 1D convolutions

The most straightforward way to do it is to feed the raw wave to a cascade of 1D
convolutions and finally produce the class probabilities.

![](https://cdn-images-1.medium.com/max/800/1*FPy_p68HZLvO8JhUT2_sOA.png)

Second Approach : Log-Mel spectrogram

One more advanced approach to audio classification is using Mel-spectrogram
instead of raw audio wave.

Mel spectrogram “is a representation of the short-term power spectrum of a
sound, based on a linear cosine transform of a log power spectrum on a nonlinear
mel scale of frequency.” —
[https://en.wikipedia.org/wiki/Mel-frequency_cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum)

Mel spectrogram transform the input raw sequence to a 2D feature map where one
dimension represents time and the other one represents frequency and the values
represents amplitude.

Results (Mean Average Precision @ 3 ) :

1D : 0.754

2D : 0.849

Average predictions of the two :0.883

2D mel Model outperforms the 1D raw wave model but the average of the two
outperforms each individual model significantly. This is probably because each
model learns different representations and make different kind of mistakes and
by averaging them each model corrects the errors of the other in some way.

Code to reproduce the results is available at :
[https://github.com/cvxtz/audio_classification](https://github.com/myouness/audio_classification)

audio_classification

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