Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks trained end-to-end on raw audio can outperform traditional feature-engineering methods in automatic musical instrument recognition within polyphonic music.

Contribution

It introduces an end-to-end CNN approach for instrument recognition, eliminating the need for manual feature engineering and domain-specific knowledge.

Findings

CNN achieves higher accuracy than traditional methods

End-to-end training simplifies the pipeline

Raw audio input is effective for instrument classification

Abstract

Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.