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Conference Papers Year : 2016

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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Dates and versions

hal-01255074 , version 1 (13-01-2016)
hal-01255074 , version 2 (23-02-2016)

Identifiers

  • HAL Id : hal-01255074 , version 2

Cite

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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