Using beamforming algorithm and large array of microphones (128 and 256 MEMS microphones) - Megamicros project
Using supervised and unsupervised Machine Learning algorithms.
Examples : Road traffic monitoring, construction site monitoring, etc.
Noise annoyance estimation (perceptual experience) and modelling.
Psychoacoustic indices modelling implementations in Python.
In INTER-NOISE (2016)
Leiba R, Ollivier F, Marchiano R, Misdariis N and Marchal J
Acoustique & Technique., Nov., 2017.
Leiba R, Ollivier F, Marchal J, Misdariis N and Marchiano R
Noise, especially road traffic noise, is cited by many studies as a source of major societal concern. So far, public responses are based only on energy quantification of sound exposure, often by measuring or estimating LA or Lden, and sound-level reduction related decision are taken. Nevertheless, psychoacoustic studies have shown that the sound level explains only a small part of the perceived noise annoyance. It is interesting to have more information about the source of noise and not to reduce the information to its sound level. In this thesis a tool is proposed for estimating the noise annoyance induced by each road vehicle using its audio signal and noise annoyance models. To do so, the audio signal of the vehicle is isolated by using inverse methods, large microphones arrays and image processing to obtain its trajectory. The knowledge of the trajectory and of the signal allows the vehicle to be classified by a machine learning method according to Morel et al. taxonomy. Once its category obtained, the specific annoyance of the vehicle is estimated thanks to a noise annoyance model using psychoacoustic and energetic indices. This allows the estimation of specific noise annoyance for each vehicle within the road traffic. The application of this method is made during a measurement day on a large Parisian artery.
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