This project's aim is to build a Convolutional Neural Network to classify songs according to the music genre. The implementation of this network uses Keras with Tensorflow Backend.
The dataset is composed by the top 50 songs of each genre (as per dataset/genres.txt) downloaded from SoundCloud. The songs are classified in folders according to the genre.
The input data of the network consists of 1 second audio frames, sliced from each song after applying amplitude normalization on the entire piece. The sampling rate of the audios is set to 44.1kHz, resampling any audio that originally came with a different sampling rate.
At the time of writing these lines, the dataset has not been compiled yet.
The network architecture from this work consists of the following stages:
A time-frequency representation is extracted from the raw audio input by computing the spectrogram using Kapre.
This section will display the results. This project is still under development so there are no conclusive results yet.
This project is licensed under the MIT License.
MIT License
Copyright (c) 2019 Héctor Martel
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