Kenichi Miyamoto

Harmonic and Percussive Sound Separation and Its Application to MIR-Related Tasks

In this chapter, we present a simple and fast method to separate a monaural audio signal into harmonic and percussive components, which leads to a useful pre-processing for MIR-related tasks. Exploiting the anisotropies of the power spectrograms of harmonic and percussive components, we define objective functions based on spectrogram gradients, and, applying to them the auxiliary function approach, we derive simple and fast update equations which guarantee the decrease of the objective function at each iteration. We show experimental results for sound separation on popular and jazz music pieces, and also present the application of the proposed technique to automatic chord recognition and rhythm-pattern extraction.

Harmonic-Temporal-Timbral Clustering (HTTC) for the analysis of multi-instrument polyphonic music signals

In this paper, we discuss a new approach named Harmonic-Temporal-Timbral Clustering (HTTC) for the analysis of single- channel audio signal of multi-instrument polyphonic music to estimate the pitch, onset timing, power and duration of all the acoustic events and to classify them into timbre categories simultaneously. Each acoustic event is modeled by a harmonic structure and a smooth envelope both represented by Gaussian mixtures. Based on the similarity between these spectro- temporal structures, timbres are clustered to form timbre categories. The entire process is mathematically formulated as a minimization problem for the I-divergence between the HTTC parametric model and the observed spectrogram of the music audio signal to simultaneously update harmonic, temporal and timbral model parameters through the EM algorithm. Some experimental results are presented to discuss the performance of the algorithm.