On Musical Onset Detection via the S-Transform

TL;DR

This paper introduces a novel musical onset detection method using the S-transform, which effectively isolates frequency subbands and achieves performance comparable to more complex statistical methods.

Contribution

The paper presents a purely temporal/spectral approach based on the S-transform for onset detection, offering improved frequency resolution and computational efficiency.

Findings

Effective isolation of frequency subbands using S-transform

Performance comparable to resource-intensive methods

Less computationally demanding approach

Abstract

Musical onset detection is a key component in any beat tracking system. Existing onset detection methods are based on temporal/spectral analysis, or methods that integrate temporal and spectral information together with statistical estimation and machine learning models. In this paper, we propose a method to localize onset components in music by using the S-transform, and thus, the method is purely based on temporal/spectral data. Unlike the other methods based on temporal/spectral data, which usually rely short time Fourier transform (STFT), our method enables effective isolation of crucial frequency subbands due to the frequency dependent resolution of S-transform. Moreover, numerical results show, even with less computationally intensive steps, the proposed method can closely resemble the performance of more resource intensive statistical estimation based approaches.