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Audio and Acoustic Signal Processing

Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)


With the strong growth of assistive and personal listening devices, natural sound rendering over headphones is becoming a necessity for prolonged listening in multimedia and virtual reality applications. The aim of natural sound rendering is to naturally recreate the sound scenes with the spatial and timbral quality as natural as possible, so as to achieve a truly immersive listening experience. However, rendering natural sound over headphones encounters many challenges. This tutorial article presents signal processing techniques to tackle these challenges to assist human listening.

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Authors:
Kaushik Sunder, Ee-Leng Tan
Submitted On:
23 February 2016 - 1:43pm
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[1] Kaushik Sunder, Ee-Leng Tan, "Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/167. Accessed: Nov. 12, 2019.
@article{167-15,
url = {http://sigport.org/167},
author = {Kaushik Sunder; Ee-Leng Tan },
publisher = {IEEE SigPort},
title = {Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)},
year = {2015} }
TY - EJOUR
T1 - Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)
AU - Kaushik Sunder; Ee-Leng Tan
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/167
ER -
Kaushik Sunder, Ee-Leng Tan. (2015). Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides). IEEE SigPort. http://sigport.org/167
Kaushik Sunder, Ee-Leng Tan, 2015. Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides). Available at: http://sigport.org/167.
Kaushik Sunder, Ee-Leng Tan. (2015). "Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)." Web.
1. Kaushik Sunder, Ee-Leng Tan. Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides) [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/167

WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION


Speaker segmentation is an essential part of any diarization system.Applications of diarization include tasks such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker environments.This paper proposes a multiple hypothesis tracking (MHT) method that exploits the harmonic structure associated with the pitch in voiced speech in order to segment the onsets and end-points of speech from multiple, overlapping speakers.

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Authors:
Aidan O. T. Hogg, Christine Evers, Patrick A. Naylor
Submitted On:
16 October 2019 - 7:03am
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[1] Aidan O. T. Hogg, Christine Evers, Patrick A. Naylor, "WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4874. Accessed: Nov. 12, 2019.
@article{4874-19,
url = {http://sigport.org/4874},
author = {Aidan O. T. Hogg; Christine Evers; Patrick A. Naylor },
publisher = {IEEE SigPort},
title = {WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION},
year = {2019} }
TY - EJOUR
T1 - WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION
AU - Aidan O. T. Hogg; Christine Evers; Patrick A. Naylor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4874
ER -
Aidan O. T. Hogg, Christine Evers, Patrick A. Naylor. (2019). WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION. IEEE SigPort. http://sigport.org/4874
Aidan O. T. Hogg, Christine Evers, Patrick A. Naylor, 2019. WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION. Available at: http://sigport.org/4874.
Aidan O. T. Hogg, Christine Evers, Patrick A. Naylor. (2019). "WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION." Web.
1. Aidan O. T. Hogg, Christine Evers, Patrick A. Naylor. WASPAA 2019 POSTER: MULTIPLE HYPOTHESIS TRACKING FOR OVERLAPPING SPEAKER SEGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4874

Selective Hearing: A Machine Listening Perspective


Selective hearing (SH) refers to the listeners' capability to focus their attention on a specific sound source or a group of sound sources in their auditory scene. This in turn implies that the listeners' focus is minimized for sources that are of no interest.
This paper describes the current landscape of machine listening research, and outlines ways in which these technologies can be leveraged to achieve SH with computational means.

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Authors:
Estefanía Cano, Hanna Lukashevich
Submitted On:
26 September 2019 - 8:41pm
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MMSP_poster.pdf

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[1] Estefanía Cano, Hanna Lukashevich, "Selective Hearing: A Machine Listening Perspective", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4845. Accessed: Nov. 12, 2019.
@article{4845-19,
url = {http://sigport.org/4845},
author = {Estefanía Cano; Hanna Lukashevich },
publisher = {IEEE SigPort},
title = {Selective Hearing: A Machine Listening Perspective},
year = {2019} }
TY - EJOUR
T1 - Selective Hearing: A Machine Listening Perspective
AU - Estefanía Cano; Hanna Lukashevich
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4845
ER -
Estefanía Cano, Hanna Lukashevich. (2019). Selective Hearing: A Machine Listening Perspective. IEEE SigPort. http://sigport.org/4845
Estefanía Cano, Hanna Lukashevich, 2019. Selective Hearing: A Machine Listening Perspective. Available at: http://sigport.org/4845.
Estefanía Cano, Hanna Lukashevich. (2019). "Selective Hearing: A Machine Listening Perspective." Web.
1. Estefanía Cano, Hanna Lukashevich. Selective Hearing: A Machine Listening Perspective [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4845

Learning Multiple Sound Source 2D Localization


In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound sources in an enclosed environment by using multiple microphone arrays. To this end, we use an encoding-decoding architecture and propose two improvements on it to accomplish the task. In addition, we also propose two novel localization representations which increase the accuracy.

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Authors:
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante
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29 September 2019 - 4:58am
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[1] Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante, "Learning Multiple Sound Source 2D Localization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4835. Accessed: Nov. 12, 2019.
@article{4835-19,
url = {http://sigport.org/4835},
author = {Guillaume Le Moing; Phongtharin Vinayavekhin; Tadanobu Inoue; Jayakorn Vongkulbhisal; Asim Munawar; Ryuki Tachibana; Don Joven Agravante },
publisher = {IEEE SigPort},
title = {Learning Multiple Sound Source 2D Localization},
year = {2019} }
TY - EJOUR
T1 - Learning Multiple Sound Source 2D Localization
AU - Guillaume Le Moing; Phongtharin Vinayavekhin; Tadanobu Inoue; Jayakorn Vongkulbhisal; Asim Munawar; Ryuki Tachibana; Don Joven Agravante
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4835
ER -
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante. (2019). Learning Multiple Sound Source 2D Localization. IEEE SigPort. http://sigport.org/4835
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante, 2019. Learning Multiple Sound Source 2D Localization. Available at: http://sigport.org/4835.
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante. (2019). "Learning Multiple Sound Source 2D Localization." Web.
1. Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante. Learning Multiple Sound Source 2D Localization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4835

Poster Imperceptible Audio Communication


A differential acoustic OFDM technique is presented to embed data imperceptibly in existing music. The method allows playing back music containing the data with a speaker without users noticing the embedded data channel. Using a microphone, the data can be recovered from the recording. Experiments with smartphone microphones show that transmission distances of 24 meters are possible, while achieving bit error ratios of less than 10 percent, depending on the environment.

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Authors:
Manuel Eichelberger, Simon Tanner, Gabriel Voirol, Roger Wattenhofer
Submitted On:
22 May 2019 - 8:47am
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Poster ICASSP 2019.pdf

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[1] Manuel Eichelberger, Simon Tanner, Gabriel Voirol, Roger Wattenhofer, "Poster Imperceptible Audio Communication", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4557. Accessed: Nov. 12, 2019.
@article{4557-19,
url = {http://sigport.org/4557},
author = {Manuel Eichelberger; Simon Tanner; Gabriel Voirol; Roger Wattenhofer },
publisher = {IEEE SigPort},
title = {Poster Imperceptible Audio Communication},
year = {2019} }
TY - EJOUR
T1 - Poster Imperceptible Audio Communication
AU - Manuel Eichelberger; Simon Tanner; Gabriel Voirol; Roger Wattenhofer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4557
ER -
Manuel Eichelberger, Simon Tanner, Gabriel Voirol, Roger Wattenhofer. (2019). Poster Imperceptible Audio Communication. IEEE SigPort. http://sigport.org/4557
Manuel Eichelberger, Simon Tanner, Gabriel Voirol, Roger Wattenhofer, 2019. Poster Imperceptible Audio Communication. Available at: http://sigport.org/4557.
Manuel Eichelberger, Simon Tanner, Gabriel Voirol, Roger Wattenhofer. (2019). "Poster Imperceptible Audio Communication." Web.
1. Manuel Eichelberger, Simon Tanner, Gabriel Voirol, Roger Wattenhofer. Poster Imperceptible Audio Communication [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4557

ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION

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Authors:
Yuzhong Wu, Tan Lee
Submitted On:
21 May 2019 - 12:14pm
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[1] Yuzhong Wu, Tan Lee, "ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4556. Accessed: Nov. 12, 2019.
@article{4556-19,
url = {http://sigport.org/4556},
author = {Yuzhong Wu; Tan Lee },
publisher = {IEEE SigPort},
title = {ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION},
year = {2019} }
TY - EJOUR
T1 - ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION
AU - Yuzhong Wu; Tan Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4556
ER -
Yuzhong Wu, Tan Lee. (2019). ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION. IEEE SigPort. http://sigport.org/4556
Yuzhong Wu, Tan Lee, 2019. ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION. Available at: http://sigport.org/4556.
Yuzhong Wu, Tan Lee. (2019). "ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION." Web.
1. Yuzhong Wu, Tan Lee. ENHANCING SOUND TEXTURE IN CNN-BASED ACOUSTIC SCENE CLASSIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4556

BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING


The intelligibility of speech in noise can be improved by modifying the speech. But with object-based audio, there
is the possibility of altering the background sound while leaving the speech unaltered. This may prove a less intrusive approach, affording good speech intelligibility without overly compromising the perceived sound quality. In this

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Authors:
Yan Tang, Qingju Liu, Bruno Fazenda, Weuwu Wang
Submitted On:
14 May 2019 - 2:49am
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[1] Yan Tang, Qingju Liu, Bruno Fazenda, Weuwu Wang, "BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4501. Accessed: Nov. 12, 2019.
@article{4501-19,
url = {http://sigport.org/4501},
author = {Yan Tang; Qingju Liu; Bruno Fazenda; Weuwu Wang },
publisher = {IEEE SigPort},
title = {BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING},
year = {2019} }
TY - EJOUR
T1 - BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING
AU - Yan Tang; Qingju Liu; Bruno Fazenda; Weuwu Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4501
ER -
Yan Tang, Qingju Liu, Bruno Fazenda, Weuwu Wang. (2019). BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING. IEEE SigPort. http://sigport.org/4501
Yan Tang, Qingju Liu, Bruno Fazenda, Weuwu Wang, 2019. BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING. Available at: http://sigport.org/4501.
Yan Tang, Qingju Liu, Bruno Fazenda, Weuwu Wang. (2019). "BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING." Web.
1. Yan Tang, Qingju Liu, Bruno Fazenda, Weuwu Wang. BACKGROUND ADAPTATION FOR IMPROVED LISTENING EXPERIENCE IN BROADCASTING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4501

In-Car Driver Authentication Using Wireless Sensing


Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics.

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Authors:
Beibei Wang
Submitted On:
13 May 2019 - 11:17am
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[1] Beibei Wang, "In-Car Driver Authentication Using Wireless Sensing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4487. Accessed: Nov. 12, 2019.
@article{4487-19,
url = {http://sigport.org/4487},
author = {Beibei Wang },
publisher = {IEEE SigPort},
title = {In-Car Driver Authentication Using Wireless Sensing},
year = {2019} }
TY - EJOUR
T1 - In-Car Driver Authentication Using Wireless Sensing
AU - Beibei Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4487
ER -
Beibei Wang. (2019). In-Car Driver Authentication Using Wireless Sensing. IEEE SigPort. http://sigport.org/4487
Beibei Wang, 2019. In-Car Driver Authentication Using Wireless Sensing. Available at: http://sigport.org/4487.
Beibei Wang. (2019). "In-Car Driver Authentication Using Wireless Sensing." Web.
1. Beibei Wang. In-Car Driver Authentication Using Wireless Sensing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4487

wav2letter++ : A Fast Open-Source Speech Recognition Framework


This paper introduces wav2letter++, a fast open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2x faster than other optimized frameworks for training end-to-end neural networks for speech recognition.

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Authors:
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert
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13 May 2019 - 8:40am
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[1] Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert, "wav2letter++ : A Fast Open-Source Speech Recognition Framework", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4483. Accessed: Nov. 12, 2019.
@article{4483-19,
url = {http://sigport.org/4483},
author = {Vineel Pratap; Awni Hannun; Qiantong Xu; Jeff Cai; Jacob Kahn; Gabriel Synnaeve; Vitaliy Liptchinsky; Ronan Collobert },
publisher = {IEEE SigPort},
title = {wav2letter++ : A Fast Open-Source Speech Recognition Framework},
year = {2019} }
TY - EJOUR
T1 - wav2letter++ : A Fast Open-Source Speech Recognition Framework
AU - Vineel Pratap; Awni Hannun; Qiantong Xu; Jeff Cai; Jacob Kahn; Gabriel Synnaeve; Vitaliy Liptchinsky; Ronan Collobert
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4483
ER -
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. (2019). wav2letter++ : A Fast Open-Source Speech Recognition Framework. IEEE SigPort. http://sigport.org/4483
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert, 2019. wav2letter++ : A Fast Open-Source Speech Recognition Framework. Available at: http://sigport.org/4483.
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. (2019). "wav2letter++ : A Fast Open-Source Speech Recognition Framework." Web.
1. Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. wav2letter++ : A Fast Open-Source Speech Recognition Framework [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4483

Adversarial Speaker Adaptation


We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model.

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Authors:
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:26pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Adversarial Speaker Adaptation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4475. Accessed: Nov. 12, 2019.
@article{4475-19,
url = {http://sigport.org/4475},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Adaptation},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Adaptation
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4475
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Adaptation. IEEE SigPort. http://sigport.org/4475
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Adaptation. Available at: http://sigport.org/4475.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Adaptation." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Adversarial Speaker Adaptation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4475

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