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Jazz Solo Instrument Classification with Convolutional Neural Networks, Source Separation, and Transfer Learning

 
: Gomez, Juan S.; Abeßer, Jakob; Cano, Estefanía

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Fulltext (PDF; )

Gomez, E. ; International Society for Music Information Retrieval -ISMIR-:
19th International Society for Music Information Retrieval Conference, ISMIR 2018. Proceedings : Paris, France, September 23-27, 2018
Paris, 2018
ISBN: 978-2-9540351-2-3
pp.577-584
International Society for Music Information Retrieval (ISMIR Conference) <19, 2018, Paris>
English
Conference Paper, Electronic Publication
Fraunhofer IDMT ()

Abstract
Predominant instrument recognition in ensemble recordings remains a challenging task, particularly if closely related instruments such as alto and tenor saxophone need to be distinguished. In this paper, we build upon a recently proposed instrument recognition algorithm based on a hybrid deep neural network: a combination of convolutional and fully connected layers for learning characteristic spectral-temporal patterns. We systematically evaluate harmonic/percussive and solo/accompaniment source separation algorithms as pre-processing steps to reduce the overlap among multiple instruments prior to the instrument recognition step. For the particular use-case of solo instrument recognition in jazz ensemble recordings, we further apply transfer learning techniques to fine-tune a previously trained instrument recognition model for classifying six jazz solo instruments. Our results indicate that both source separation as pre-processing step as well as transfer learning clearly improve recognition performance, especially for smaller subsets of highly similar instruments.

: http://publica.fraunhofer.de/documents/N-534558.html