This is the companion webpage for the paper
A Study of Instrument-wise Onset Detection in Beijing Opera Percussion Ensembles
Mi Tian, Ajay Srinivasamurthy, Mark Sandler, Xavier Serra
Proceedings of the 39th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) (pp. 2174–2178), 2014, Florence, Italy
Abstract: Note onset detection and instrument recognition are two of the most investigated tasks in Music Information Retrieval (MIR). Various detection methods have been proposed in previous research for western music, with less focus on other music cultures of the world. In this paper, we focus on onset detection for percussion instruments in Beijing Opera, a major genre of Chinese traditional music. A dataset of individual audio samples of four primary percussion instruments is used to obtain the spectral bases for each instrument. With these bases, we separate the input percussion ensemble recordings into its spectral sources and their activations using a Non-negative Matrix Factorization (NMF) based algorithm. A simple onset detection conducted on each NMF activation presents us satisfactory overall detection rates, and provides us valuable implications and suggestions for future development of drum transcription and percussion pattern analysis in Beijing Opera.