Automatic Classification of Music Genre using Masked Conditional Neural Networks

TL;DR

This paper introduces Masked Conditional Neural Networks (MCLNN), a novel architecture tailored for sound recognition that leverages frequency band learning and systematic sparsity, achieving superior music genre classification performance.

Contribution

The paper presents MCLNN, a new neural network architecture that incorporates masking to focus on frequency bands, improving robustness and feature exploration in music genre classification.

Findings

MCLNN outperforms state-of-the-art CNNs on the Ballroom dataset.

MCLNN achieves competitive results with fewer parameters.

Masking enhances frequency-shift robustness.

Abstract

Neural network based architectures used for sound recognition are usually adapted from other application domains such as image recognition, which may not harness the time-frequency representation of a signal. The ConditionaL Neural Networks (CLNN) and its extension the Masked ConditionaL Neural Networks (MCLNN) are designed for multidimensional temporal signal recognition. The CLNN is trained over a window of frames to preserve the inter-frame relation, and the MCLNN enforces a systematic sparseness over the network's links that mimics a filterbank-like behavior. The masking operation induces the network to learn in frequency bands, which decreases the network susceptibility to frequency-shifts in time-frequency representations. Additionally, the mask allows an exploration of a range of feature combinations concurrently analogous to the manual handcrafting of the optimum collection of features for a recognition task. MCLNN have achieved competitive performance on the Ballroom music dataset compared to several hand-crafted attempts and outperformed models based on state-of-the-art Convolutional Neural Networks.