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Title: Exploiting piano acoustics in automatic transcription
Author: Cheng, Tian
ISNI:       0000 0004 5991 4771
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2016
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In this thesis we exploit piano acoustics to automatically transcribe piano recordings into a symbolic representation: the pitch and timing of each detected note. To do so we use approaches based on non-negative matrix factorisation (NMF). To motivate the main contributions of this thesis, we provide two preparatory studies: a study of using a deterministic annealing EM algorithm in a matrix factorisation-based system, and a study of decay patterns of partials in real-word piano tones. Based on these studies, we propose two generative NMF-based models which explicitly model different piano acoustical features. The first is an attack/decay model, that takes into account the time-varying timbre and decaying energy of piano sounds. The system divides a piano note into percussive attack and harmonic decay stages, and separately models the two parts using two sets of templates and amplitude envelopes. The two parts are coupled by the note activations. We simplify the decay envelope by an exponentially decaying function. The proposed method improves the performance of supervised piano transcription. The second model aims at using the spectral width of partials as an independent indicator of the duration of piano notes. Each partial is represented by a Gaussian function, with the spectral width indicated by the standard deviation. The spectral width is large in the attack part, but gradually decreases to a stable value and remains constant in the decay part. The model provides a new aspect to understand the time-varying timbre of piano notes, but furtherinvestigation is needed to use it effectively to improve piano transcription. We demonstrate the utility of the proposed systems in piano music transcription and analysis. Results show that explicitly modelling piano acoustical features, especially temporal features, can improve the transcription performance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: piano acoustics ; non-negative matrix factorisation ; transcription performance