Music Genre Classification by Machine Learning Algorithms

Abstract

Working with audio data has been a machine learning issue that has received relatively little attention. With the fast emerging of machine learning and deep learning algorithms, benchmarks for the most recent groundbreaking deep learning research are frequently determined by how well it performs on text and image data. Additionally, models that use text and graphics are where deep learning has made the biggest strides. Speech and audio, equally significant data types, are frequently ignored in this. Audio data has many uses in daily life, ranging from converting spoken words to text, live translation, and employing audio and music for analysis or filtering. A crucial component of music information retrieval systems, content-based music genre classification has featured prominently as a consequence of the emergence of digital music on the Internet. The field of automatically classifying music genres is still incredibly young, and the claimed classification performance levels are currently relatively low.