Acoustic Event Detection: SVM-Based System and Evaluation Setup in CLEAR’07

Temko, Andriy; Nadeu, Climent; Biel, Joan-Isaac

doi:10.1007/978-3-540-68585-2_34

Cited by 26 publications

(27 citation statements)

References 4 publications

(5 reference statements)

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“…Unfortunately, we were not able to explore it more due to the low size of the evaluation corpus. Future work will be devoted to the application of the FI to multi-microphone classification (and also detection) of acoustic events with a much larger dataset recorded in meeting rooms [17]. Also, a further improvement can come from the fact that signals captured from microphones placed at different positions in the room may carry different information about the acoustic events taking place in it.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy integral based information fusion for classification of highly confusable non-speech sounds

Temko

Macho

Nadeu

2008

Pattern Recognition

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Acoustic event classification may help to describe acoustic scenes and contribute to improve the robustness of speech technologies. In this work, fusion of different information sources with the Fuzzy Integral (FI), and the associated Fuzzy Measure (FM), are applied to the problem of classifying a small set of highly confusable human non-speech sounds. As FI is a meaningful formalism for combining classifier outputs that can capture interactions among the various sources of information, it shows in our experiments a significantly better performance than that of any single classifier entering the FI fusion module. Actually, that FI decision-level fusion approach shows comparable results to the highperforming SVM feature-level fusion and thus it seems to be a good choice when feature-level fusion is not an option. We have also observed that the importance and the degree of interaction among the various feature types given by the FM can be used for feature selection, and gives a valuable insight into the problem.Keywords: Acoustic event classification, audio features, fuzzy integral and fuzzy measure, feature-level and decision-level information fusion, feature selection, interaction of information sources, Choquet integral.

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Section: Resultsmentioning

confidence: 99%

Fuzzy integral based information fusion for classification of highly confusable non-speech sounds

Temko

Macho

Nadeu

2008

Pattern Recognition

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“…In our experiments, the CLEAR'07 evaluation database is used [2]. It consists of 25 interactive seminars, approximately 30min-long that have been recorded by AIT, ITC, IBM, UKA, and UPC in their smart-rooms.…”

Section: Database and Metricmentioning

confidence: 99%

“…The SVM-based AED system used in the present work is the one that was also used for the AED evaluations in CLEAR 2007 [2] with slight modifications. The sound signal from a single MarkIII array microphone is down-sampled to 16 kHz, and framed (frame length/shift is 30/10ms, a Hamming window is used).…”

Section: Svm-based Aed Systemmentioning

confidence: 99%

“…Recently, several papers have reported works on acoustic events detection (AED) for different meeting-room environments and databases [1] [2]. The CLEAR'07 international evaluations in seminar conditions have shown that AED is a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

“…The CLEAR'07 international evaluations in seminar conditions have shown that AED is a challenging problem. In fact, 5 out of 6 submitted systems showed accuracy below 25%, and the best system got 33.6% accuracy (see [2] [3] for results, databases and metrics). The single main factor that accounts for those low detection scores is the high degree of overlap between sounds, especially between the targeted acoustic events and speech.…”

Section: Introductionmentioning

confidence: 99%

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Inclusion of Video Information for Detection of Acoustic Events Using the Fuzzy Integral

Butko¹,

Temko²,

Nadeu³

et al.

Machine Learning for Multimodal Interaction

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Abstract. When applied to interactive seminars, the detection of acoustic events from only audio information shows a large amount of errors, which are mostly due to the temporal overlaps of sounds. Video signals may be a useful additional source of information to cope with that problem for particular events. In this work, we aim at improving the detection of steps by using two audiobased AED systems, with SVM and HMM, and a video-based AED system, which employs the output of a 3D video tracking algorithm. Fuzzy integral is used to fuse the outputs of the three detection systems. Experimental results using the CLEAR 2007 evaluation data show that video information can be successfully used to improve the results of audio-based AED.

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Environmental sound processing and its applications

Miyazaki

Toda

Hayashi

et al. 2019

IEEJ Transactions Elec Engng

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As part of the effort to develop techniques for understanding environments using sound, many studies in the field of computational auditory scene analysis have focused on using computers to perform functions carried out naturally by the human auditory system. Thanks to recent progress in machine‐learning techniques, these environmental sound‐processing techniques have significantly improved and a widening variety of applications has resulted in considerable interest in this field. In this review, we introduce the fundamental techniques of environmental sound processing, as well as recent advances in front‐end and back‐end processing and potential applications for these techniques. Prospects for further progress in the field of environmental sound processing and the challenges still to be overcome are also discussed. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Acoustic Event Detection: SVM-Based System and Evaluation Setup in CLEAR’07

Cited by 26 publications

References 4 publications

Fuzzy integral based information fusion for classification of highly confusable non-speech sounds

Fuzzy integral based information fusion for classification of highly confusable non-speech sounds

Inclusion of Video Information for Detection of Acoustic Events Using the Fuzzy Integral

Environmental sound processing and its applications

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