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Appl. Sci. 2016, 6(6), 162; doi:10.3390/app6060162

Metrics for Polyphonic Sound Event Detection

Department of Signal Processing, Tampere University of Technology, P.O. Box 553, Tampere FI-33101, Finland
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Author to whom correspondence should be addressed.
Academic Editor: Vesa Valimaki
Received: 26 February 2016 / Revised: 22 April 2016 / Accepted: 18 May 2016 / Published: 25 May 2016
(This article belongs to the Special Issue Audio Signal Processing)
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Abstract

This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics. View Full-Text
Keywords: pattern recognition; audio signal processing; audio content analysis; computational auditory scene analysis; sound events; everyday sounds; polyphonic sound event detection; evaluation of sound event detection pattern recognition; audio signal processing; audio content analysis; computational auditory scene analysis; sound events; everyday sounds; polyphonic sound event detection; evaluation of sound event detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mesaros, A.; Heittola, T.; Virtanen, T. Metrics for Polyphonic Sound Event Detection. Appl. Sci. 2016, 6, 162.

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