Audiovisual laughter detection based on temporal features

Stavros Petridis; Maja  Pantic

Audiovisual laughter detection based on temporal features

Stavros Petridis, Maja Pantic

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

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Abstract

Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved performance over single-modal approaches. Static features are extracted on an audio/video frame basis and then combined with temporal features extracted over a temporal window, describing the evolution of static features over time. When tested on 96 audiovisual sequences, depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, in a person independent way the proposed audiovisual approach achieves an F1 rate of over 89%.

Original language	English
Title of host publication	BNAIC 2008
Subtitle of host publication	Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008
Editors	Anton Nijholt, Maja Pantic, Mannes Poel, Hendri Hondorp
Publisher	University of Twente
Pages	351-352
Number of pages	2
Publication status	Published - 30 Oct 2008
Event	20th Benelux Conference on Artificial Intelligence, BNAIC 2008 - Boekelo, Netherlands Duration: 30 Oct 2008 → 31 Oct 2008 Conference number: 20

Publication series

Name	BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference
Publisher	University of Twente
Number	20
ISSN (Print)	1568-7805

Conference

Conference	20th Benelux Conference on Artificial Intelligence, BNAIC 2008
Abbreviated title	BNAIC
Country	Netherlands
City	Boekelo
Period	30/10/08 → 31/10/08

Keywords

HMI-MI: MULTIMODAL INTERACTIONS
EC Grant Agreement nr.: FP7/211486
EC Grant Agreement nr.: FP6/0027787

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Petridis2008audiovisualFinal published version, 255 KB

Cite this

Petridis, S., & Pantic, M. (2008). Audiovisual laughter detection based on temporal features. In A. Nijholt, M. Pantic, M. Poel, & H. Hondorp (Eds.), BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008 (pp. 351-352). (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; No. 20). University of Twente.

Petridis, Stavros ; Pantic, Maja . / Audiovisual laughter detection based on temporal features. BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. editor / Anton Nijholt ; Maja Pantic ; Mannes Poel ; Hendri Hondorp. University of Twente, 2008. pp. 351-352 (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; 20).

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abstract = "Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved performance over single-modal approaches. Static features are extracted on an audio/video frame basis and then combined with temporal features extracted over a temporal window, describing the evolution of static features over time. When tested on 96 audiovisual sequences, depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, in a person independent way the proposed audiovisual approach achieves an F1 rate of over 89%.",

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Petridis, S & Pantic, M 2008, Audiovisual laughter detection based on temporal features. in A Nijholt, M Pantic, M Poel & H Hondorp (eds), BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference, no. 20, University of Twente, pp. 351-352, 20th Benelux Conference on Artificial Intelligence, BNAIC 2008, Boekelo, Netherlands, 30/10/08.

Audiovisual laughter detection based on temporal features. / Petridis, Stavros; Pantic, Maja .

BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. ed. / Anton Nijholt; Maja Pantic; Mannes Poel; Hendri Hondorp. University of Twente, 2008. p. 351-352 (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; No. 20).

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

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N2 - Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved performance over single-modal approaches. Static features are extracted on an audio/video frame basis and then combined with temporal features extracted over a temporal window, describing the evolution of static features over time. When tested on 96 audiovisual sequences, depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, in a person independent way the proposed audiovisual approach achieves an F1 rate of over 89%.

AB - Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved performance over single-modal approaches. Static features are extracted on an audio/video frame basis and then combined with temporal features extracted over a temporal window, describing the evolution of static features over time. When tested on 96 audiovisual sequences, depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, in a person independent way the proposed audiovisual approach achieves an F1 rate of over 89%.

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Petridis S, Pantic M. Audiovisual laughter detection based on temporal features. In Nijholt A, Pantic M, Poel M, Hondorp H, editors, BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. University of Twente. 2008. p. 351-352. (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; 20).