Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme

Mustafa Erseven; Bulent Bolat

doi:10.1109/SIU.2018.8404639

Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme

Translated title of the contribution: Regression-based speech enhancement by convolutional neural network

Mustafa Erseven, Bulent Bolat

Yildiz Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this study, a regression-based convolutional neural network (CNN) model is proposed for speech enhancement. The main purpose is to remove the noise on the conversations. A babble noise is added to the speech samples of different persons and samples with different signal to noise ratio (SNR) are obtained. The logarithmic power spectrum (LPS) coefficients of noisy and clean speech signal samples are calculated. Then a regression model is established between the convolutional neural network and the logarithmic power spectrum coefficients of noisy and clean speech. The results are evaluated by perceptual evaluation of speech quality (PESQ) and short time objective intelligibility (STOI). The results are presented in tabular form.

Translated title of the contribution	Regression-based speech enhancement by convolutional neural network
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404639
Publication status	Published - 5 Jul 2018
Externally published	Yes
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/SIU.2018.8404639

Cite this

Erseven, M., & Bolat, B. (2018). Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404639

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title = "Evri{\c s}imsel sinir aǧi ile regresyon temelli konu{\c s}ma iyile{\c s}tirme",

abstract = "In this study, a regression-based convolutional neural network (CNN) model is proposed for speech enhancement. The main purpose is to remove the noise on the conversations. A babble noise is added to the speech samples of different persons and samples with different signal to noise ratio (SNR) are obtained. The logarithmic power spectrum (LPS) coefficients of noisy and clean speech signal samples are calculated. Then a regression model is established between the convolutional neural network and the logarithmic power spectrum coefficients of noisy and clean speech. The results are evaluated by perceptual evaluation of speech quality (PESQ) and short time objective intelligibility (STOI). The results are presented in tabular form.",

keywords = "Convolutional Neural Network, Logarithmic Power spectrum, Regression Model, Speech Enhancement",

author = "Mustafa Erseven and Bulent Bolat",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

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doi = "10.1109/SIU.2018.8404639",

language = "T{\"u}rk{\c c}e",

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--4",

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Erseven, M & Bolat, B 2018, Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404639

Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme. / Erseven, Mustafa; Bolat, Bulent.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme

AU - Erseven, Mustafa

AU - Bolat, Bulent

PY - 2018/7/5

Y1 - 2018/7/5

N2 - In this study, a regression-based convolutional neural network (CNN) model is proposed for speech enhancement. The main purpose is to remove the noise on the conversations. A babble noise is added to the speech samples of different persons and samples with different signal to noise ratio (SNR) are obtained. The logarithmic power spectrum (LPS) coefficients of noisy and clean speech signal samples are calculated. Then a regression model is established between the convolutional neural network and the logarithmic power spectrum coefficients of noisy and clean speech. The results are evaluated by perceptual evaluation of speech quality (PESQ) and short time objective intelligibility (STOI). The results are presented in tabular form.

AB - In this study, a regression-based convolutional neural network (CNN) model is proposed for speech enhancement. The main purpose is to remove the noise on the conversations. A babble noise is added to the speech samples of different persons and samples with different signal to noise ratio (SNR) are obtained. The logarithmic power spectrum (LPS) coefficients of noisy and clean speech signal samples are calculated. Then a regression model is established between the convolutional neural network and the logarithmic power spectrum coefficients of noisy and clean speech. The results are evaluated by perceptual evaluation of speech quality (PESQ) and short time objective intelligibility (STOI). The results are presented in tabular form.

KW - Convolutional Neural Network

KW - Logarithmic Power spectrum

KW - Regression Model

KW - Speech Enhancement

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DO - 10.1109/SIU.2018.8404639

M3 - Konferans katkısı

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T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

ER -

Evrişimsel sinir aǧi ile regresyon temelli konuşma iyileştirme

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