/Masters-project

Primary LanguagePython

Learning Audio Features For Genre Classification with Deep Belief Networks

Feature extraction is a crucial part of many music information retrieval(MIR) tasks. In this project, I present an end to end deep neural network that extract the features for a given audio sample, performs genre classification. The feature extraction is based on Discrete Fourier Transforms (DFTs) of the audio. The extracted features are used to train over a Deep Belief Network (DBN). A DBN is built out of multiple layers of Randomized Boltzmann Machines (RBMs), which makes the system a fully connected neural network. The same network is used for testing with a softmax layer at the end which serves as the classifier. This entire task of genre classification has been done with the Tzanetakis dataset and yielded a test accuracy of 74.6%.

Technologies used:

  • Python3

  • Keras

  • TensorFlow

  • Theano

  • NumPy

  • Pickle

  • LibROSA

  • Google Cloud Platform (GCP)

  • Developed a deep learning model/application for audio genre classification. Performed inference step with Keras with TensorFlow backend (GPU).

  • Entire application was run on a GCP virtual machine.

  • Model accuracy 74%, real time performance on CPU (<10ms) and GPU (<5ms).