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Informing Piano Multi-Pitch Estimation with Inferred Local Polyphony Based on Convolutional Neural Networks

 
: Taenzer, Michael; Mimilakis, Stylianos I.; Abeßer, Jakob

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Electronics. Online journal 10 (2021), No.7, Art. 851, 18 pp.
http://www.mdpi.com/journal/electronics
ISSN: 2079-9292
English
Journal Article, Electronic Publication
Fraunhofer IDMT ()
Convolutional Neural Networks; multi-pitch estimation; music information retrieval; polyphony estimation

Abstract
In this work, we propose considering the information from a polyphony for multi-pitch estimation (MPE) in piano music recordings. To that aim, we propose a method for local polyphony estimation (LPE), which is based on convolutional neural networks (CNNs) trained in a supervised fashion to explicitly predict the degree of polyphony. We investigate two feature representations as inputs to our method, in particular, the Constant-Q Transform (CQT) and its recent extension Folded-CQT (F-CQT). To evaluate the performance of our method, we conduct a series of experiments on real and synthetic piano recordings based on the MIDI Aligned Piano Sounds (MAPS) and the Saarland Music Data (SMD) datasets. We compare our approaches with a state-of-the art piano transcription method by informing said method with the LPE knowledge in a postprocessing stage. The experimental results suggest that using explicit LPE information can refine MPE predictions. Furthermore, it is shown that, on average, the CQT representation is preferred over F-CQT for LPE.

: http://publica.fraunhofer.de/documents/N-633968.html