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Saliency-maximized audio visualization and efficient audio-visual browsing for faster-than-real-time human acoustic event detection

Saliency-maximized audio visualization and efficient audio-visual browsing for... Saliency-Maximized Audio Visualization and Efficient Audio-Visual Browsing for Faster-Than-Real-Time Human Acoustic Event Detection KAI-HSIANG LIN, XIAODAN ZHUANG , CAMILLE GOUDESEUNE, SARAH KING, MARK HASEGAWA-JOHNSON, and THOMAS S. HUANG, University of Illinois at Urbana-Champaign Browsing large audio archives is challenging because of the limitations of human audition and attention. However, this task becomes easier with a suitable visualization of the audio signal, such as a spectrogram transformed to make unusual audio events salient. This transformation maximizes the mutual information between an isolated event's spectrogram and an estimate of how salient the event appears in its surrounding context. When such spectrograms are computed and displayed with fluid zooming over many temporal orders of magnitude, sparse events in long audio recordings can be detected more quickly and more easily. In particular, in a 1/10-real-time acoustic event detection task, subjects who were shown saliency-maximized rather than conventional spectrograms performed significantly better. Saliency maximization also improves the mutual information between the ground truth of nonbackground sounds and visual saliency, more than other common enhancements to visualization. Categories and Subject Descriptors: H.5.2 [Information Interfaces and Presentation]: User Interfaces--Theory and methods, Evaluation/methodology; H.1.2 [Models and Principles]: User/Machine Systems--Human Information Processing; H.5.1 [Information Interfaces and Presentation]: http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Applied Perception (TAP) Association for Computing Machinery

Saliency-maximized audio visualization and efficient audio-visual browsing for faster-than-real-time human acoustic event detection

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References (43)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1544-3558
DOI
10.1145/2536764.2536773
Publisher site
See Article on Publisher Site

Abstract

Saliency-Maximized Audio Visualization and Efficient Audio-Visual Browsing for Faster-Than-Real-Time Human Acoustic Event Detection KAI-HSIANG LIN, XIAODAN ZHUANG , CAMILLE GOUDESEUNE, SARAH KING, MARK HASEGAWA-JOHNSON, and THOMAS S. HUANG, University of Illinois at Urbana-Champaign Browsing large audio archives is challenging because of the limitations of human audition and attention. However, this task becomes easier with a suitable visualization of the audio signal, such as a spectrogram transformed to make unusual audio events salient. This transformation maximizes the mutual information between an isolated event's spectrogram and an estimate of how salient the event appears in its surrounding context. When such spectrograms are computed and displayed with fluid zooming over many temporal orders of magnitude, sparse events in long audio recordings can be detected more quickly and more easily. In particular, in a 1/10-real-time acoustic event detection task, subjects who were shown saliency-maximized rather than conventional spectrograms performed significantly better. Saliency maximization also improves the mutual information between the ground truth of nonbackground sounds and visual saliency, more than other common enhancements to visualization. Categories and Subject Descriptors: H.5.2 [Information Interfaces and Presentation]: User Interfaces--Theory and methods, Evaluation/methodology; H.1.2 [Models and Principles]: User/Machine Systems--Human Information Processing; H.5.1 [Information Interfaces and Presentation]:

Journal

ACM Transactions on Applied Perception (TAP)Association for Computing Machinery

Published: Oct 1, 2013

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