Machine Learning Applied to Music/Audio Signal Processing

Alexander Lerch,Peter Knees

doi:10.3390/electronics10243077

Alexander Lerch, Peter Knees

Open Access

https://doi.org/10.3390/electronics10243077

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Journal: Electronics	Publication Date: Dec 10, 2021
Citations: 3	License type: CC BY 4.0

Affiliation: Georgia Institute of Technology, TU Wien

Abstract

Over the past two decades, the utilization of machine learning in audio and music signal processing has dramatically increased [...]

Highlights

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Novel approaches in the fields of Music Information Retrieval and Audio Signal Processing are largely dominated by machine learning solutions
Due to the unique challenges posed by audio signals, including the superposition of many sources overlapping in both time and frequency and the inherent semantic and hierarchical structure, the application of generic, domainagnostic machine learning models is often not successful without modifications

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Over the past two decades, the utilization of machine learning in audio and music signal processing has dramatically increased. Novel approaches in the fields of Music Information Retrieval and Audio Signal Processing are largely dominated by machine learning solutions.

Results

Conclusion