Acoustic event detection in meeting-room environments

Temko, Andriy; Nadeu, Climent

doi:10.1016/j.patrec.2009.06.009

Cited by 78 publications

(89 citation statements)

References 12 publications

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“…The purpose of detection is to discern between the foreground events and the acoustic background, without determining whether an event is threatening or not. Some researchers use foreground/background or silence/non-silence classifiers to achieve this task [40,42]. We employ dedicated detection algorithms which do not require training and are adaptive to changing conditions.…”

Section: Methodsmentioning

confidence: 99%

“…The classification algorithm is based on the Support Vector Machine (SVM) classifier. The principles of SVM and its application to numerous fields have been studied in the literature, namely to text classification [11], face detection or acoustic event detection [35,40]. It was proven in previous work that the Support Vector Machine can be an efficient tool for the classification of signals in an audio-based surveillance system, as it robustly discerns threatening from non-threatening events [25].…”

Section: Classificationmentioning

confidence: 99%

“…Since the length of environmental audio events can vary from less than 1 second to even more than 10 seconds, a correct approach is to divide the signal into frames, classify each frame separately and subsequently make the decision. Such an approach was proposed by Temko and Nadeau [40]. In our work a frame of 200 ms in length is used and the overlap factor equals 50 %.…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Lopatka

Kotus

Czyżewski

2015

Multimed Tools Appl

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Evaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for discerning between the events being in focus and the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier are introduced. The sound source localization algorithm based on the analysis of multichannel signals from the Acoustic Vector Sensor is presented. The methods are evaluated in an experiment conducted in the anechoic chamber, in which the representative events are played together with noise of differing intensity. The results of detection, classification and localization accuracy with respect to the Signal to Noise Ratio are discussed. The results show that the recognition and localization accuracy are strongly dependent on the acoustic conditions. We also found that the engineered algorithms provide a sufficient robustness in moderately intense noise in order to be applied to practical audio-visual surveillance systems.

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Section: Methodsmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Lopatka

Kotus

Czyżewski

2015

Multimed Tools Appl

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“…Acoustic event detection has been widely applied in many real world applications, such as in surveillance systems [1], siren detection systems [2], chew event detection systems [3] and human-computer interaction [4][5] [6]. Intraclass variations and the spectral-temporal properties across classes pose great challenges to acoustic event detection.…”

Section: Introductionmentioning

confidence: 99%

Frame-Wise Dynamic Threshold Based Polyphonic Acoustic Event Detection

et al. 2017

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Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt multi-label classification techniques to perform the polyphonic acoustic event detection with a global threshold to detect the active acoustic events. However, the global threshold has to be set manually and is highly dependent on the database being tested. To deal with this, we replaced the fixed threshold method with a frame-wise dynamic threshold approach in this paper. Two novel approaches, namely contour and regressor based dynamic threshold approaches are proposed in this work. Experimental results on the popular TUT Acoustic Scenes 2016 database of polyphonic events demonstrated the superior performance of the proposed approaches.

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“…Both tasks become much more challenging when there exists sound simultaneity, i.e., several sounds overlapping in time and in a given room. For example, after the CLEAR'07 international evaluations [12], where AED was carried out with meeting room seminars, it became clear that time overlapping of acoustic events (AEs) was a major source of detection errors [13].…”

Section: Introductionmentioning

confidence: 99%

Source ambiguity resolution of overlapped sounds in a multi-microphone room environment

Chakraborty

Nadeu

Butko

2014

J AUDIO SPEECH MUSIC PROC.

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When several acoustic sources are simultaneously active in a meeting room scenario, and both the position of the sources and the identity of the time-overlapped sound classes have been estimated, the problem of assigning each source position to one of the sound classes still remains. This problem is found in the real-time system implemented in our smart-room, where it is assumed that up to two acoustic events may overlap in time and the source positions are relatively well separated in space. The position assignment system proposed in this work is based on fusion of model-based log-likelihood ratios obtained after carrying out several different partial source separations in parallel. To perform the separation, frequency-invariant null-steering beamformers, which can work with a small number of microphones, are used. The experimental results using all the six microphone arrays deployed in the room show a high assignment rate in our particular scenario.

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Acoustic event detection in meeting-room environments

Cited by 78 publications

References 12 publications

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Frame-Wise Dynamic Threshold Based Polyphonic Acoustic Event Detection

Source ambiguity resolution of overlapped sounds in a multi-microphone room environment

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