Improving Musical Instrument Classification with Advanced Machine Learning Techniques

TL;DR

This paper evaluates various machine learning techniques, including deep learning, for musical instrument classification using the NSynth dataset, highlighting their effectiveness, limitations, and potential for future improvements.

Contribution

It systematically compares traditional and deep learning methods for instrument classification and explores hybrid models, offering new insights and directions for future research.

Findings

Deep learning models outperform traditional methods in accuracy.

Hybrid models show promise for improved classification.

The study provides a comprehensive comparison of machine learning techniques.

Abstract

Musical instrument classification, a key area in Music Information Retrieval, has gained considerable interest due to its applications in education, digital music production, and consumer media. Recent advances in machine learning, specifically deep learning, have enhanced the capability to identify and classify musical instruments from audio signals. This study applies various machine learning methods, including Naive Bayes, Support Vector Machines, Random Forests, Boosting techniques like AdaBoost and XGBoost, as well as deep learning models such as Convolutional Neural Networks and Artificial Neural Networks. The effectiveness of these methods is evaluated on the NSynth dataset, a large repository of annotated musical sounds. By comparing these approaches, the analysis aims to showcase the advantages and limitations of each method, providing guidance for developing more accurate and efficient classification systems. Additionally, hybrid model testing and discussion are included. This research aims to support further studies in instrument classification by proposing new approaches and future research directions.