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Detection of Overlapping Acoustic Events using a Temporally-Constrained Probabilistic Model

Abstract : In this paper, a system for overlapping acoustic event detection is proposed, which models the temporal evolution of sound events. The system is based on probabilistic latent component analysis, supporting the use of a sound event dictionary where each exemplar consists of a succession of spectral templates. The temporal succession of the templates is controlled through event class-wise Hidden Markov Models (HMMs). As input time/frequency representation, the Equivalent Rectangular Bandwidth (ERB) spectrogram is used. Experiments are carried out on polyphonic datasets of office sounds generated using an acoustic scene synthesizer-simulator, as well as real and synthesized monophonic datasets for comparative purposes. Results show that the proposed system outperforms several state-of-the-art methods for overlapping acoustic event detection on the same task, using both frame-based and event-based metrics, and is robust to varying event density and noise levels.
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https://hal.archives-ouvertes.fr/hal-01255074
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Submitted on : Tuesday, February 23, 2016 - 9:40:12 AM
Last modification on : Wednesday, April 27, 2022 - 3:50:13 AM
Long-term archiving on: : Sunday, November 13, 2016 - 1:49:54 AM

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  • HAL Id : hal-01255074, version 2

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Emmanouil Benetos, Grégoire Lafay, Mathieu Lagrange, Mark D. Plumbley. Detection of Overlapping Acoustic Events using a Temporally-Constrained Probabilistic Model. ICASSP, Mar 2016, Shanghai, China. ⟨hal-01255074v2⟩

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