Singing voice separation using deep neural networks and f0 estimation

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

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 proceeding › Conference 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 language	English
Title of host publication	MIREX 2016
Number of pages	2
Publication status	Published - 2016
Externally published	Yes
Event	12th Music Information Retrieval Evaluation Exchange - New York City, United States Duration: 7 Aug 2016 → 12 Aug 2016 Conference number: 12 http://www.music-ir.org/mirex/wiki/2016:Main_Page (Link to Conference Website)

Conference

Conference	12th Music Information Retrieval Evaluation Exchange
Abbreviated title	MIREX 2016
Country/Territory	United States
City	New York City
Period	7/08/16 → 12/08/16
Internet address	http://www.music-ir.org/mirex/wiki/2016:Main_Page (Link to Conference Website)

Access to Document

https://www.music-ir.org/mirex/abstracts/2016/RSGP1.pdfLicence: CC BY-NC-SA

Cite this

@inproceedings{d27f968126d74d669e2d65d5885239e1,

title = "Singing voice separation using deep neural networks and f0 estimation",

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.",

author = "Gerard Roma and Grais, {Emad M} and Simpson, {Andrew JR} and Plumbley, {Mark D}",

year = "2016",

language = "English",

booktitle = "MIREX 2016",

note = "12th Music Information Retrieval Evaluation Exchange, MIREX 2016 ; Conference date: 07-08-2016 Through 12-08-2016",

url = "http://www.music-ir.org/mirex/wiki/2016:Main_Page",

}

TY - GEN

T1 - Singing voice separation using deep neural networks and f0 estimation

AU - Roma, Gerard

AU - Grais, Emad M

AU - Simpson, Andrew JR

AU - Plumbley, Mark D

N1 - Conference code: 12

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.music-ir.org/mirex/wiki/2016:Singing_Voice_Separation_Results

M3 - Conference contribution

BT - MIREX 2016

T2 - 12th Music Information Retrieval Evaluation Exchange

Y2 - 7 August 2016 through 12 August 2016

ER -

Singing voice separation using deep neural networks and f0 estimation

Abstract

Conference

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Cite this