Sparse representations of polyphonic music
Signal Processing - Sparse approximations in signal and image processing
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
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Polyphonic transcription could be formulated as a supervised classification task if the classifiers of all possible polyphonic combinations could be learned beforehand. However, it is impractical to learn all possible classification models in real life due to the exponential explosion of all possible polyphonic combinations. Here, we describe a novel polyphonic transcription approach that applies a hybrid of the Particle Swarm Optimisation (PSO) and the Tone-model techniques. This hybrid approach exploits the strengths from both the heuristic-search and the model based approaches. In our work, only the monophonic Tone-models of all pitches are learned and employed to calculate the first pass output of polyphonic transcription, which is then refined in the second pass by PSO. The experimental results show that the proposed hybrid approach outperform the competing Non-negative Matrix Factorisation (NMF) approach. This paper presents and discusses the design and the experimental results of this novel approach.