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Spatial and Multichannel Audio

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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SPM15slides_Natural Sound Rendering for Headphones.pdf

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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: Sep. 20, 2018.
@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

Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering

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Authors:
Sina Hafezi, Alastair H. Moore, Patrick A. Naylor
Submitted On:
9 July 2018 - 7:12pm
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DBSCAN.pdf

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[1] Sina Hafezi, Alastair H. Moore, Patrick A. Naylor, "Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3378. Accessed: Sep. 20, 2018.
@article{3378-18,
url = {http://sigport.org/3378},
author = {Sina Hafezi; Alastair H. Moore; Patrick A. Naylor },
publisher = {IEEE SigPort},
title = {Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering},
year = {2018} }
TY - EJOUR
T1 - Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering
AU - Sina Hafezi; Alastair H. Moore; Patrick A. Naylor
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3378
ER -
Sina Hafezi, Alastair H. Moore, Patrick A. Naylor. (2018). Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering. IEEE SigPort. http://sigport.org/3378
Sina Hafezi, Alastair H. Moore, Patrick A. Naylor, 2018. Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering. Available at: http://sigport.org/3378.
Sina Hafezi, Alastair H. Moore, Patrick A. Naylor. (2018). "Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering." Web.
1. Sina Hafezi, Alastair H. Moore, Patrick A. Naylor. Robust Source Counting and Acoustic DOA Estimation using Density-based Clustering [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3378

Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain


A gridless sound field decomposition method based on the reciprocity gap functional (RGF) is proposed. An intuitive and powerful way of reconstructing a sound field inside a region including sound sources is to decompose the sound field into Green's functions. Current methods based on sparse representation require discretization of the reconstruction region into grid points to construct the dictionary matrix; however, this procedure causes an off-grid problem and has a high computational cost.

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Authors:
Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari
Submitted On:
8 July 2018 - 11:01am
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main.pdf

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[1] Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari, "Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3372. Accessed: Sep. 20, 2018.
@article{3372-18,
url = {http://sigport.org/3372},
author = {Yuhta Takida; Shoichi Koyama; Natsuki Ueno; Hiroshi Saruwatari },
publisher = {IEEE SigPort},
title = {Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain},
year = {2018} }
TY - EJOUR
T1 - Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain
AU - Yuhta Takida; Shoichi Koyama; Natsuki Ueno; Hiroshi Saruwatari
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3372
ER -
Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari. (2018). Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain. IEEE SigPort. http://sigport.org/3372
Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari, 2018. Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain. Available at: http://sigport.org/3372.
Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari. (2018). "Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain." Web.
1. Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari. Gridless sound field decomposition based on reciprocity gap functional in spherical harmonic domain [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3372

Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks


In this paper, we propose a rate-distributed linearly constrained minimum variance (LCMV) beamformer for joint noise reduction and spatial cue preservation for assistive hearing in wireless acoustic sensor networks (WASNs). The WASN can consist of wireless communicating hearing aids, extended with additional wireless microphones. Due to the fact that each sensor node has a limited power budget, it is essential to consider the energy usage when designing algorithms for such WASNs.

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Authors:
Jie Zhang, Richard Heusdens, Richard C. Hendriks
Submitted On:
5 July 2018 - 3:01am
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Jie_SAMslides_tudelft.pdf

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[1] Jie Zhang, Richard Heusdens, Richard C. Hendriks, "Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3353. Accessed: Sep. 20, 2018.
@article{3353-18,
url = {http://sigport.org/3353},
author = {Jie Zhang; Richard Heusdens; Richard C. Hendriks },
publisher = {IEEE SigPort},
title = {Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks},
year = {2018} }
TY - EJOUR
T1 - Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks
AU - Jie Zhang; Richard Heusdens; Richard C. Hendriks
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3353
ER -
Jie Zhang, Richard Heusdens, Richard C. Hendriks. (2018). Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks. IEEE SigPort. http://sigport.org/3353
Jie Zhang, Richard Heusdens, Richard C. Hendriks, 2018. Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks. Available at: http://sigport.org/3353.
Jie Zhang, Richard Heusdens, Richard C. Hendriks. (2018). "Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks." Web.
1. Jie Zhang, Richard Heusdens, Richard C. Hendriks. Rate-Distributed Binaural LCMV Beamforming for Assistive Hearing in Wireless Acoustic Sensor Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3353

CASCADE: Channel-Aware Structured Cosparse Audio DEclipper

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Authors:
Clément Gaultier, Nancy Bertin, Rémi Gribonval
Submitted On:
25 April 2018 - 4:14am
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CASCADE

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[1] Clément Gaultier, Nancy Bertin, Rémi Gribonval, "CASCADE: Channel-Aware Structured Cosparse Audio DEclipper", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3175. Accessed: Sep. 20, 2018.
@article{3175-18,
url = {http://sigport.org/3175},
author = {Clément Gaultier; Nancy Bertin; Rémi Gribonval },
publisher = {IEEE SigPort},
title = {CASCADE: Channel-Aware Structured Cosparse Audio DEclipper},
year = {2018} }
TY - EJOUR
T1 - CASCADE: Channel-Aware Structured Cosparse Audio DEclipper
AU - Clément Gaultier; Nancy Bertin; Rémi Gribonval
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3175
ER -
Clément Gaultier, Nancy Bertin, Rémi Gribonval. (2018). CASCADE: Channel-Aware Structured Cosparse Audio DEclipper. IEEE SigPort. http://sigport.org/3175
Clément Gaultier, Nancy Bertin, Rémi Gribonval, 2018. CASCADE: Channel-Aware Structured Cosparse Audio DEclipper. Available at: http://sigport.org/3175.
Clément Gaultier, Nancy Bertin, Rémi Gribonval. (2018). "CASCADE: Channel-Aware Structured Cosparse Audio DEclipper." Web.
1. Clément Gaultier, Nancy Bertin, Rémi Gribonval. CASCADE: Channel-Aware Structured Cosparse Audio DEclipper [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3175

Pyroomacoustics: A Python package for audio room simulation and array processing algorithms


We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms.

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Authors:
Robin Scheibler, Eric Bezzam, Ivan Dokmanic
Submitted On:
23 April 2018 - 4:18am
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[1] Robin Scheibler, Eric Bezzam, Ivan Dokmanic, "Pyroomacoustics: A Python package for audio room simulation and array processing algorithms", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3146. Accessed: Sep. 20, 2018.
@article{3146-18,
url = {http://sigport.org/3146},
author = {Robin Scheibler; Eric Bezzam; Ivan Dokmanic },
publisher = {IEEE SigPort},
title = {Pyroomacoustics: A Python package for audio room simulation and array processing algorithms},
year = {2018} }
TY - EJOUR
T1 - Pyroomacoustics: A Python package for audio room simulation and array processing algorithms
AU - Robin Scheibler; Eric Bezzam; Ivan Dokmanic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3146
ER -
Robin Scheibler, Eric Bezzam, Ivan Dokmanic. (2018). Pyroomacoustics: A Python package for audio room simulation and array processing algorithms. IEEE SigPort. http://sigport.org/3146
Robin Scheibler, Eric Bezzam, Ivan Dokmanic, 2018. Pyroomacoustics: A Python package for audio room simulation and array processing algorithms. Available at: http://sigport.org/3146.
Robin Scheibler, Eric Bezzam, Ivan Dokmanic. (2018). "Pyroomacoustics: A Python package for audio room simulation and array processing algorithms." Web.
1. Robin Scheibler, Eric Bezzam, Ivan Dokmanic. Pyroomacoustics: A Python package for audio room simulation and array processing algorithms [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3146

MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION


Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required.

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Authors:
Yuki Mitsufuji, Thushara Abhayapala
Submitted On:
20 April 2018 - 5:10pm
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ICASSP2018_poster.pdf

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[1] Yuki Mitsufuji, Thushara Abhayapala, "MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3115. Accessed: Sep. 20, 2018.
@article{3115-18,
url = {http://sigport.org/3115},
author = {Yuki Mitsufuji; Thushara Abhayapala },
publisher = {IEEE SigPort},
title = {MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION},
year = {2018} }
TY - EJOUR
T1 - MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION
AU - Yuki Mitsufuji; Thushara Abhayapala
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3115
ER -
Yuki Mitsufuji, Thushara Abhayapala. (2018). MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION. IEEE SigPort. http://sigport.org/3115
Yuki Mitsufuji, Thushara Abhayapala, 2018. MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION. Available at: http://sigport.org/3115.
Yuki Mitsufuji, Thushara Abhayapala. (2018). "MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION." Web.
1. Yuki Mitsufuji, Thushara Abhayapala. MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3115

MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS


We present a source separation system for high-order ambisonics (HOA) contents. We derive a multichannel spatial filter from a mask estimated by a long short-term memory (LSTM) recurrent neural network. We combine one channel of the mixture with the outputs of basic HOA beamformers as inputs to the LSTM, assuming that we know the directions of arrival of the directional sources. In our experiments, the speech of interest can be corrupted either by diffuse noise or by an equally loud competing speaker.

perotin.pdf

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Authors:
Emmanuel Vincent, Alexandre Guérin
Submitted On:
19 April 2018 - 5:18pm
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[1] Emmanuel Vincent, Alexandre Guérin, "MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3031. Accessed: Sep. 20, 2018.
@article{3031-18,
url = {http://sigport.org/3031},
author = {Emmanuel Vincent; Alexandre Guérin },
publisher = {IEEE SigPort},
title = {MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS},
year = {2018} }
TY - EJOUR
T1 - MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS
AU - Emmanuel Vincent; Alexandre Guérin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3031
ER -
Emmanuel Vincent, Alexandre Guérin. (2018). MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS. IEEE SigPort. http://sigport.org/3031
Emmanuel Vincent, Alexandre Guérin, 2018. MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS. Available at: http://sigport.org/3031.
Emmanuel Vincent, Alexandre Guérin. (2018). "MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS." Web.
1. Emmanuel Vincent, Alexandre Guérin. MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3031

Spatial audio feature discovery with convolutional neural networks


The advent of mixed reality consumer products brings about a pressing need to develop and improve spatial sound rendering techniques for a broad user base. Despite a large body of prior work, the precise nature and importance of various sound localization cues and how they should be personalized for an individual user to improve localization performance is still an open research problem. Here we propose training a convolutional neural network (CNN) to classify the elevation angle of spatially rendered sounds and employing Layerwise Relevance Propagation (LRP) on the trained CNN model.

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Authors:
Etienne Thuillier, Hannes Gamper, Ivan J. Tashev
Submitted On:
30 May 2018 - 7:50am
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Spatial_audio_feature_discovery_ICASSP_2018.pdf

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[1] Etienne Thuillier, Hannes Gamper, Ivan J. Tashev, "Spatial audio feature discovery with convolutional neural networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2975. Accessed: Sep. 20, 2018.
@article{2975-18,
url = {http://sigport.org/2975},
author = {Etienne Thuillier; Hannes Gamper; Ivan J. Tashev },
publisher = {IEEE SigPort},
title = {Spatial audio feature discovery with convolutional neural networks},
year = {2018} }
TY - EJOUR
T1 - Spatial audio feature discovery with convolutional neural networks
AU - Etienne Thuillier; Hannes Gamper; Ivan J. Tashev
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2975
ER -
Etienne Thuillier, Hannes Gamper, Ivan J. Tashev. (2018). Spatial audio feature discovery with convolutional neural networks. IEEE SigPort. http://sigport.org/2975
Etienne Thuillier, Hannes Gamper, Ivan J. Tashev, 2018. Spatial audio feature discovery with convolutional neural networks. Available at: http://sigport.org/2975.
Etienne Thuillier, Hannes Gamper, Ivan J. Tashev. (2018). "Spatial audio feature discovery with convolutional neural networks." Web.
1. Etienne Thuillier, Hannes Gamper, Ivan J. Tashev. Spatial audio feature discovery with convolutional neural networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2975

Considerations regarding individualization of head-related transfer functions


This paper provides some considerations regarding using individualized head-related transfer functions for rendering binaural spatial audio over headphones. It briefly considers the degree of benefit that individualization may provide. It then examines the degree of variation existing within the ear morphology across listeners within the Sydney-York Morphological and Recording of Ears (SYMARE) database using kernel principal component analysis and the large deformation diffeomorphic metric mapping framework.

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Authors:
Reza Zolfaghari, Xian Long, Arun Sebastian, Shayikh Hossain, Alexis Glaunes, Anthony Tew, Muhammad Shahnawaz, Augusto Sarti
Submitted On:
18 April 2018 - 4:22pm
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CJinICASSP2018.pdf

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[1] Reza Zolfaghari, Xian Long, Arun Sebastian, Shayikh Hossain, Alexis Glaunes, Anthony Tew, Muhammad Shahnawaz, Augusto Sarti, "Considerations regarding individualization of head-related transfer functions", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2972. Accessed: Sep. 20, 2018.
@article{2972-18,
url = {http://sigport.org/2972},
author = {Reza Zolfaghari; Xian Long; Arun Sebastian; Shayikh Hossain; Alexis Glaunes; Anthony Tew; Muhammad Shahnawaz; Augusto Sarti },
publisher = {IEEE SigPort},
title = {Considerations regarding individualization of head-related transfer functions},
year = {2018} }
TY - EJOUR
T1 - Considerations regarding individualization of head-related transfer functions
AU - Reza Zolfaghari; Xian Long; Arun Sebastian; Shayikh Hossain; Alexis Glaunes; Anthony Tew; Muhammad Shahnawaz; Augusto Sarti
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2972
ER -
Reza Zolfaghari, Xian Long, Arun Sebastian, Shayikh Hossain, Alexis Glaunes, Anthony Tew, Muhammad Shahnawaz, Augusto Sarti. (2018). Considerations regarding individualization of head-related transfer functions. IEEE SigPort. http://sigport.org/2972
Reza Zolfaghari, Xian Long, Arun Sebastian, Shayikh Hossain, Alexis Glaunes, Anthony Tew, Muhammad Shahnawaz, Augusto Sarti, 2018. Considerations regarding individualization of head-related transfer functions. Available at: http://sigport.org/2972.
Reza Zolfaghari, Xian Long, Arun Sebastian, Shayikh Hossain, Alexis Glaunes, Anthony Tew, Muhammad Shahnawaz, Augusto Sarti. (2018). "Considerations regarding individualization of head-related transfer functions." Web.
1. Reza Zolfaghari, Xian Long, Arun Sebastian, Shayikh Hossain, Alexis Glaunes, Anthony Tew, Muhammad Shahnawaz, Augusto Sarti. Considerations regarding individualization of head-related transfer functions [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2972

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