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Fast numerical and machine learning ...

Luo, Yuancheng.

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Fast numerical and machine learning algorithms for spatial audio reproduction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fast numerical and machine learning algorithms for spatial audio reproduction./
Author:	Luo, Yuancheng.
Description:	215 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
Contained By:	Dissertation Abstracts International76-03B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3644152
ISBN:	9781321322101

Fast numerical and machine learning algorithms for spatial audio reproduction.
Luo, Yuancheng.

Fast numerical and machine learning algorithms for spatial audio reproduction. - 215 p.

Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2014.

This item must not be sold to any third party vendors.

Audio reproduction technologies have underwent several revolutions from a purely mechanical, to electromagnetic, and into a digital process. These changes have resulted in steady improvements in the objective qualities of sound capture/playback on increasingly portable devices. However, most mobile playback devices remove important spatialdirectional components of externalized sound which are natural to the subjective experience of human hearing. Fortunately, the missing spatial-directional parts can be integrated back into audio through a combination of computational methods and physical knowledge of how sound scatters off of the listener's anthropometry in the sound-field. The former employs signal processing techniques for rendering the sound-field. The latter employs approximations of the sound-field through the measurement of so-called Head-Related Impulse Responses/Transfer Functions (HRIRs/HRTFs).

ISBN: 9781321322101Subjects--Topical Terms:

626642
Computer Science.

Fast numerical and machine learning algorithms for spatial audio reproduction.
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100 1 $a Luo, Yuancheng. $3 3170157
245 1 0 $a Fast numerical and machine learning algorithms for spatial audio reproduction.
300 $a 215 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
500 $a Adviser: Ramani Duraiswami.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Audio reproduction technologies have underwent several revolutions from a purely mechanical, to electromagnetic, and into a digital process. These changes have resulted in steady improvements in the objective qualities of sound capture/playback on increasingly portable devices. However, most mobile playback devices remove important spatialdirectional components of externalized sound which are natural to the subjective experience of human hearing. Fortunately, the missing spatial-directional parts can be integrated back into audio through a combination of computational methods and physical knowledge of how sound scatters off of the listener's anthropometry in the sound-field. The former employs signal processing techniques for rendering the sound-field. The latter employs approximations of the sound-field through the measurement of so-called Head-Related Impulse Responses/Transfer Functions (HRIRs/HRTFs).
520 $a This dissertation develops several numerical and machine learning algorithms for accelerating and personalizing spatial audio reproduction in light of available mobile computing power. First, spatial audio synthesis between a sound-source and sound-field requires fast convolution algorithms between the audio-stream and the HRIRs. We introduce a novel sparse decomposition algorithm for HRIRs based on non-negative matrix factorization that allows for faster time-domain convolution than frequency-domain fast- Fourier-transform variants. Second, the full sound-field over the spherical coordinate domain must be efficiently approximated from a finite collection of HRTFs. We develop a joint spatial-frequency covariance model for Gaussian process regression (GPR) and sparse-GPR methods that supports the fast interpolation and data fusion of HRTFs across multiple data-sets. Third, the direct measurement of HRTFs requires specialized equipment that is unsuited for widespread acquisition. We "bootstrap" the human ability to localize sound in listening tests with Gaussian process active-learning techniques over graphical user interfaces that allows the listener to infer his/her own HRTFs. Experiments are conducted on publicly available HRTF datasets and human listeners.
590 $a School code: 0117.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Mathematics. $3 515831
650 4 $a Artificial Intelligence. $3 769149
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710 2 $a University of Maryland, College Park. $b Computer Science. $3 1018451
773 0 $t Dissertation Abstracts International $g 76-03B(E).
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791 $a Ph.D.
792 $a 2014
793 $a English
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