Multiview approaches to event detection and scene analysis

Slim Essid; Sanjeel Parekh; Ngoc Q.K. Duong; Romain Serizel; Alexey Ozerov; Fabio Antonacci; Augusto Sarti

doi:10.1007/978-3-319-63450-0_9

Multiview approaches to event detection and scene analysis

Slim Essid, Sanjeel Parekh, Ngoc Q.K. Duong, Romain Serizel, Alexey Ozerov, Fabio Antonacci, Augusto Sarti

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

Abstract

This chapter addresses sound scene and event classification in multiview settings, that is, settings where the observations are obtained from multiple sensors, each sensor contributing a particular view of the data (e.g., audio microphones, video cameras, etc.). We briefly introduce some of the techniques that can be exploited to effectively combine the data conveyed by the different views under analysis for a better interpretation. We first provide a high-level presentation of generic methods that are particularly relevant in the context of multiview and multimodal sound scene analysis. Then, we more specifically present a selection of techniques used for audiovisual event detection and microphone array-based scene analysis.

Original language	English
Title of host publication	Computational Analysis of Sound Scenes and Events
Publisher	Springer International Publishing
Pages	243-276
Number of pages	34
ISBN (Electronic)	9783319634500
ISBN (Print)	9783319634494
DOIs	https://doi.org/10.1007/978-3-319-63450-0_9
Publication status	Published - 21 Sept 2017
Externally published	Yes

Keywords

Audio source localization and tracking
Audio source separation
Beamforming
Data fusion
Joint audiovisual scene analysis
Matrix factorization
Multichannel Wiener filtering
Multichannel audio
Multimodal scene analysis
Multiview scene analysis
Representation learning
Tensor factorization

Access to Document

10.1007/978-3-319-63450-0_9

Cite this

@inbook{09577bffc2c1450784d6b8638f99d668,

title = "Multiview approaches to event detection and scene analysis",

abstract = "This chapter addresses sound scene and event classification in multiview settings, that is, settings where the observations are obtained from multiple sensors, each sensor contributing a particular view of the data (e.g., audio microphones, video cameras, etc.). We briefly introduce some of the techniques that can be exploited to effectively combine the data conveyed by the different views under analysis for a better interpretation. We first provide a high-level presentation of generic methods that are particularly relevant in the context of multiview and multimodal sound scene analysis. Then, we more specifically present a selection of techniques used for audiovisual event detection and microphone array-based scene analysis.",

keywords = "Audio source localization and tracking, Audio source separation, Beamforming, Data fusion, Joint audiovisual scene analysis, Matrix factorization, Multichannel Wiener filtering, Multichannel audio, Multimodal scene analysis, Multiview scene analysis, Representation learning, Tensor factorization",

author = "Slim Essid and Sanjeel Parekh and Duong, \{Ngoc Q.K.\} and Romain Serizel and Alexey Ozerov and Fabio Antonacci and Augusto Sarti",

year = "2017",

month = sep,

day = "21",

doi = "10.1007/978-3-319-63450-0\_9",

language = "English",

isbn = "9783319634494",

pages = "243--276",

booktitle = "Computational Analysis of Sound Scenes and Events",

publisher = "Springer International Publishing",

}

TY - CHAP

T1 - Multiview approaches to event detection and scene analysis

AU - Essid, Slim

AU - Parekh, Sanjeel

AU - Duong, Ngoc Q.K.

AU - Serizel, Romain

AU - Ozerov, Alexey

AU - Antonacci, Fabio

AU - Sarti, Augusto

PY - 2017/9/21

Y1 - 2017/9/21

N2 - This chapter addresses sound scene and event classification in multiview settings, that is, settings where the observations are obtained from multiple sensors, each sensor contributing a particular view of the data (e.g., audio microphones, video cameras, etc.). We briefly introduce some of the techniques that can be exploited to effectively combine the data conveyed by the different views under analysis for a better interpretation. We first provide a high-level presentation of generic methods that are particularly relevant in the context of multiview and multimodal sound scene analysis. Then, we more specifically present a selection of techniques used for audiovisual event detection and microphone array-based scene analysis.

AB - This chapter addresses sound scene and event classification in multiview settings, that is, settings where the observations are obtained from multiple sensors, each sensor contributing a particular view of the data (e.g., audio microphones, video cameras, etc.). We briefly introduce some of the techniques that can be exploited to effectively combine the data conveyed by the different views under analysis for a better interpretation. We first provide a high-level presentation of generic methods that are particularly relevant in the context of multiview and multimodal sound scene analysis. Then, we more specifically present a selection of techniques used for audiovisual event detection and microphone array-based scene analysis.

KW - Audio source localization and tracking

KW - Audio source separation

KW - Beamforming

KW - Data fusion

KW - Joint audiovisual scene analysis

KW - Matrix factorization

KW - Multichannel Wiener filtering

KW - Multichannel audio

KW - Multimodal scene analysis

KW - Multiview scene analysis

KW - Representation learning

KW - Tensor factorization

U2 - 10.1007/978-3-319-63450-0_9

DO - 10.1007/978-3-319-63450-0_9

M3 - Chapter

AN - SCOPUS:85034959444

SN - 9783319634494

SP - 243

EP - 276

BT - Computational Analysis of Sound Scenes and Events

PB - Springer International Publishing

ER -

Multiview approaches to event detection and scene analysis

Abstract

Keywords

Access to Document

Fingerprint

Cite this