Singing voice separation using deep neural networks and f0 estimation

Gerard Roma, Emad M Grais, Andrew JR Simpson, Mark D Plumbley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep Neural Networks (DNN) have become a popular approach for speech enhancement, and singing voice separation. DNNs are typically trained to estimate a time frequency mask using ground truth examples. In this submission, we combine DNN estimation as a first step with traditional refinement via F0 estimation, using the YINFFT algorithm.
Original languageEnglish
Title of host publicationMIREX 2016
Number of pages2
Publication statusPublished - 2016
Externally publishedYes
Event12th Music Information Retrieval Evaluation Exchange - New York City, United States
Duration: 7 Aug 201612 Aug 2016
Conference number: 12
http://www.music-ir.org/mirex/wiki/2016:Main_Page (Link to Conference Website)

Conference

Conference12th Music Information Retrieval Evaluation Exchange
Abbreviated titleMIREX 2016
CountryUnited States
CityNew York City
Period7/08/1612/08/16
Internet address

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Cite this

Roma, G., Grais, E. M., Simpson, A. JR., & Plumbley, M. D. (2016). Singing voice separation using deep neural networks and f0 estimation. In MIREX 2016