Exploring Music Genre Classification: Algorithm Analysis and Deployment Architecture

TL;DR

This paper introduces a novel DSP and Deep Learning-based algorithm for music genre classification, tested on GTZAN dataset, and proposes an end-to-end deployment architecture for integration into music applications.

Contribution

It presents a new combined DSP and Deep Learning algorithm for genre classification and an architecture for deploying it in real-world music apps.

Findings

High accuracy achieved on GTZAN dataset

Effective integration of DSP and DL techniques

Promising approach for real-world deployment

Abstract

Music genre classification has become increasingly critical with the advent of various streaming applications. Nowadays, we find it impossible to imagine using the artist's name and song title to search for music in a sophisticated music app. It is always difficult to classify music correctly because the information linked to music, such as region, artist, album, or non-album, is so variable. This paper presents a study on music genre classification using a combination of Digital Signal Processing (DSP) and Deep Learning (DL) techniques. A novel algorithm is proposed that utilizes both DSP and DL methods to extract relevant features from audio signals and classify them into various genres. The algorithm was tested on the GTZAN dataset and achieved high accuracy. An end-to-end deployment architecture is also proposed for integration into music-related applications. The performance of the algorithm is analyzed and future directions for improvement are discussed. The proposed DSP and DL-based music genre classification algorithm and deployment architecture demonstrate a promising approach for music genre classification.