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Road traffic sound level estimation from realistic urban sound mixtures by Non-negative Matrix Factorization

Résumé : Experimental acoustic sensor networks are currently tested in large cities, and appear more and more as a useful tool to enrich modeled road traffic noise maps through data assimilation techniques. One challenge is to be able to isolate from the measured sound mixtures acoustic quantities of interest such as the sound level of road traffic. This task is anything but trivial because of the multiple sound sources that overlap within urban sound mixtures. In this paper, the Non-negative Matrix Factorization (NMF) framework is developed to estimate road traffic noise levels within urban sound scenes. To evaluate the performances of the proposed approach, a synthetic corpus of sound scenes is designed, to cover most common soundscape settings, and whom realism is validated through a perceptual test. The simulated scenes reproduce then the sensor network outputs, in which the actual occurrence and sound level of each source are known. Several variants of NMF are tested. The proposed approach, named threshold initialized NMF, appears to be the most reliable approach, allowing road traffic noise level estimation with average errors of less than 1.3 dB over the tested corpus of sound scenes.
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Submitted on : Thursday, October 4, 2018 - 1:55:18 PM
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Jean-Rémy Gloaguen, Arnaud Can, Mathieu Lagrange, Jean-François Petiot. Road traffic sound level estimation from realistic urban sound mixtures by Non-negative Matrix Factorization. Applied Acoustics, Elsevier, 2019, pp.229-238. ⟨10.1016/j.apacoust.2018.08.018⟩. ⟨hal-01887710⟩

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