Music Genre Classification using Masked Conditional Neural Networks

TL;DR

This paper introduces the Masked Conditional Neural Network (MCLNN), a model designed to improve music genre classification by capturing time-frequency representations and automating feature exploration, achieving competitive accuracy.

Contribution

The paper presents MCLNN, a novel neural network architecture that enforces systematic sparsity and automates feature selection for music genre classification.

Findings

MCLNN achieves competitive accuracy with state-of-the-art methods.

The mask enforces frequency band learning, improving robustness to shifts.

Automated feature exploration reduces manual tuning effort.

Abstract

The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The CLNN captures the conditional temporal influence between the frames in a window and the mask in the MCLNN enforces a systematic sparseness that follows a filterbank-like pattern over the network links. The mask induces the network to learn about time-frequency representations in bands, allowing the network to sustain frequency shifts. Additionally, the mask in the MCLNN automates the exploration of a range of feature combinations, usually done through an exhaustive manual search. We have evaluated the MCLNN performance using the Ballroom and Homburg datasets of music genres. MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.