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Data-driven solo voice enhancement for jazz music retrieval

 
: Balke, Stefan; Dittmar, Christian; Abeßer, Jakob; Müller, Meinard

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017. Proceedings : March 5-9, 2017, Hilton New Orleans Riverside, New Orleans, Louisiana, USA
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-4117-6
ISBN: 978-1-5090-4116-9
ISBN: 978-1-5090-4118-3
pp.196-200
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <42, 2017, New Orleans/La.>
English
Conference Paper
Fraunhofer IDMT ()

Abstract
Retrieving short monophonic queries in music recordings is a challenging research problem in Music Information Retrieval (MIR). In jazz music, given a solo transcription, one retrieval task is to find the corresponding (potentially polyphonic) recording in a music collection. Many conventional systems approach such retrieval tasks by first extracting the predominant F0-trajectory from the recording, then quantizing the extracted trajectory to musical pitches and finally comparing the resulting pitch sequence to the monophonic query. In this paper, we introduce a data-driven approach that avoids the hard decisions involved in conventional approaches: Given pairs of time-frequency (TF) representations of full music recordings and TF representations of solo transcriptions, we use a DNN-based approach to learn a mapping for transforming a “polyphonic” TF representation into a “monophonic” TF representation. This transform can be considered as a kind of solo voice enhancement. We evaluate our approach within a jazz solo retrieval scenario and compare it to a state-of-the-art method for predominant melody extraction.

: http://publica.fraunhofer.de/documents/N-461583.html