CLEAR Evaluation of Acoustic Event Detection and Classification Systems

Temko, Andriy; Malkin, Robert; Zieger, Christian; Macho, Dušan; Nadeu, Climent; Omologo, Maurizio

doi:10.1007/978-3-540-69568-4_29

Cited by 90 publications

(95 citation statements)

References 5 publications

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“…Intraclass variations and the spectral-temporal properties across classes pose great challenges to acoustic event detection. Due to the significant real world applications of AED and the challenges being faced, some campaigns, such as CLEAR [7] and D-CASE [8] [9] have attempted to capture the wide range of variations in the design of the acoustic event detection databases [10][11] [12].…”

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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Section: Introductionmentioning

confidence: 99%

Frame-Wise Dynamic Threshold Based Polyphonic Acoustic Event Detection

et al. 2017

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“…There are some significant techniques for these applications; acoustic event detection (AED) analyzes various types of sounds, e.g., "footsteps," "running water," "music," "voice," to detect specific types of sounds [6], [7], and acoustic scene analysis (ASA) analyzes acoustic scenes such as user activities, e.g., "cooking," "vacuuming," "watching TV," or situations, e.g., "being on the bus," "being in a park," "meeting," on the basis of the information of acoustic events [8]. In this paper, we focus on automatic estimation of acoustic scenes, which enables automatic life-logging, the monitoring of elderly people, or multimedia retrieval.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Scene Analysis Based on Hierarchical Generative Model of Acoustic Event Sequence

Imoto

Shimauchi

2016

IEICE Trans. Inf. & Syst.

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“…In the task of acoustic event detection (AED), for example, machines perform poorly when detecting and labeling non-speech events in long audio recordings: none of the systems competing in the CLEAR 2007 AED competition achieved greater than 30% accuracy [Temko et al 2006;Temko 2007;Zhou et al 2007]. This AED task is difficult even for humans, because of the set of events selected and the low SNR, e.g., chair squeak and door creak are difficult to detect, let alone distinguish, in common seminar audio recordings.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Zhuang

Goudeseune

et al. 2013

ACM Trans. Appl. Percept.

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Examining large audio archives is a challenging task for humans owing to the limitations of human audition. We explore an innovative approach to engage both human vision and audition for audio browsing, which significantly improves human acoustic event detection in long audio recordings. In particular we visualize the data as a saliency-maximized spectrogram, accessed at different temporal scales using a special audio browser that also allows rapid zooming across scales from hours to milliseconds.The saliency-maximized audio spectrogram lets humans quickly search for and detect events in audio recordings. By rendering target events as visually salient patterns, this representation minimizes the time and effort needed to visually examine a recording. This transformation maximizes the mutual information between the spectrogram of an isolated target event and the estimated saliency of the overall visual representation.When subjects are shown spectrograms that are saliency-maximized instead of the original spectrograms, they perform significantly better in a 1/10-real-time acoustic event detection task.

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CLEAR Evaluation of Acoustic Event Detection and Classification Systems

Cited by 90 publications

References 5 publications

Frame-Wise Dynamic Threshold Based Polyphonic Acoustic Event Detection

Frame-Wise Dynamic Threshold Based Polyphonic Acoustic Event Detection

Acoustic Scene Analysis Based on Hierarchical Generative Model of Acoustic Event Sequence

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

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