  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.
