Bimodal sound source tracking applied to road traffic monitoring

The constant increase of road traffic requires closer and closer road network monitoring. The awareness of traffic characteristics in real time as well as its historical trends, facilitates decision-making for flow regulation, triggering relief operations, ensuring the motorists’ safety and contribute to optimize transport infrastructures. Today, the heterogeneity of the available data makes their processing complex and expensive (multiple sensors with different technologies, placed in different locations, with their own data format, unsynchronized, etc.). This leads metrologists to develop “smarter” monitoring devices, i.e. capable of providing all the necessary data synchronized from a single measurement point, with no impact on the flow road itself and ideally without complex installation. This work contributes to achieve such an objective through the development of a passive, compact, non-intrusive, acoustic-based system composed of a microphone array with a few number of elements placed on the roadside. The proposed signal processing techniques enable vehicle detection, the estimation of their speed as well as the estimation of their wheelbase length as they pass by. Sound sources emitted by tyre/road interactions are localized using generalized cross-correlation functions between sensor pairs. These successive correlation measurements are filtered using a sequential Monte Carlo method (particle filter) enabling, on one hand, the simultaneous tracking of multiple vehicles (that follow or pass each other) and on the other hand, a discrimination between useful sound sources and interfering noises. This document focuses on two-axle road vehicles only. The two tyre/road interactions (front and rear) observed by a microphone array on the roadside are modeled as two stochastic, zero-mean and uncorrelated processes, spatially disjoint by the wheelbase length. This bimodal sound source model defines a specific particle filter, called bimodal particle filter, which is presented here. Compared to the classical (unimodal) particle filter, a better robustness for speed estimation is achieved especially in cases of harsh observation. Moreover the proposed algorithm enables the wheelbase length estimation through purely passive acoustic measurement. An innovative microphone array design methodology, based on a mathematical expression of the observation and the tracking methodology itself is also presented. The developed algorithms are validated and assessed through in-situ measurements. Estimates provided by the acoustical signal processing are compared with standard radar measurements and confronted to video monitoring images. Although presented in a purely road-related applied context, we feel that the developed methodologies can be, at least partly, applied to rail, aerial, underwater or industrial metrology

Sensor array optimization for sources separation and detection in the at-worst determined case

Most of the number of sources estimation techniques use the well-known signal-subspace approach in which the number of dominant sources is deduced regarding the multiplicity of the lowest eigenvalues of the correlation matrix. In the at-worst determined case (number of microphones just equals the maximal number of possible radiating sources) such methods are inoperative because the noise subspace could be inexistant. However, a well chosen sensor array geometry permits to achieve source detection using eigenvalues above conditions to some a priori knowledge on the sources. This paper explores some relation between geometry and eigenvalues in order to achieve optimal sources detection and separation. This study yields analytical formulations of both optimisation problem by working on the simple case of two uncorrelated harmonic sources. Theoretical and experimental measurements are presented and discussed

Audio Novelty-Based Segmentation of Music Concerts

The Swiss Federal Institute of Technology in Lausanne (EPFL) is in the process of digitizing an exceptional collection of audio and video recordings of the Montreux Jazz Festival (MJF) concerts. Since 1967, five thousand hours of both audio and video have been recorded with about 60% digitized so far. In order to make these archives easily manageable, ensure the correctness of the supplied metadata, and facilitate copyright management, one of the desired tasks is to know exactly how many songs are present in a given concert, and identify them individually, even in very problematic cases (such as medleys or long improvisational periods). However, due to the sheer amount of recordings to process, it is a quite cumbersome and time consuming task to have a person listen to each concert and identify every song. Consequently, it is essential to automate the process. To that end, this paper describes a strategy for automatically detecting the most important changes in an audio file of concert; for MJF concerts, those changes correspond to song transitions, interludes, or applause. The presented method belongs to the family of audio novelty-based segmentation methods. The general idea is to first divide a whole concert into short frames, each of a few milliseconds length, from which well-chosen audio features are extracted. Then, a similarity matrix is computed which provides information about the similarities between each pair of frames. Next, a kernel is correlated along the diagonal of the similarity matrix to determine the audio novelty scores. Finally, peak detection is used to find significant peaks in the scores which are suggestive of a change. The main advantage of such a method is that no training step is required as opposed to most of the classical segmentation algorithms. Additionally, relatively few audio features are needed which leads to a reduction in the amount of computation and run time. It is expected that such a preprocessing shall speed up the song identification process: instead of having to listen to hours of music, the algorithm will produce markings to indicate where to start listening. The presented method is evaluated using real concert recordings that have been segmented by hand; and its performance is compared to the state-of-the-art

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

This paper focuses on road traffic monitoring using sounds and proposes, more specifically, a microphone array design methodology for observing vehicle trajectory from acoustic-based correlation functions. In a former work, authors have shown that combining generalized cross correlation (GCC) functions and a particle filter onto the audio signals simultaneously acquired by two sensors placed near the road allows the joint estimation of the speed and the wheelbase length of road vehicles as they pass by. This is mainly due to the broadband nature of the tire/road noise, which makes their spatial dissociation possible by means of an appropriate GCC processor. At the time, nothing has been said about the best distance to chose between the sensors. A methodology is proposed here to find this optimum, which is expected to improve the observation quality and, thus, the tracking performance. Theoretical developments of this paper are partially assessed with preliminary experiments

Body part-centered and full body-centered peripersonal space representations

Dedicated neural systems represent the space surrounding the body, termed Peripersonal space (PPS), by integrating visual or auditory stimuli occurring near the body with somatosensory information. As a behavioral proxy to PPS, we measured participants' reaction time to tactile stimulation while task-irrelevant auditory or visual stimuli were presented at different distances from their body. In 7 experiments we delineated the critical distance at which auditory or visual stimuli boosted tactile processing on the hand, face, and trunk as a proxy of the PPS extension. Three main findings were obtained. First, the size of PPS varied according to the stimulated body part, being progressively bigger for the hand, then face, and largest for the trunk. Second, while approaching stimuli always modulated tactile processing in a space-dependent manner, receding stimuli did so only for the hand. Finally, the extension of PPS around the hand and the face varied according to their relative positioning and stimuli congruency, whereas the trunk PPS was constant. These results suggest that at least three body-part specific PPS representations exist, differing in extension and directional tuning. These distinct PPS representations, however, are not fully independent from each other, but referenced to the common reference frame of the trunk

Acoustic User Experiences for the Montreux Jazz Lab

The Montreux Jazz Festival archives covers almost a half-century of live jazz, rock and pop music, gathering international first-class musicians within a single catalogue. In the frame of the digitization and valorisation of the whole archives by EPFL research groups, the question arose on how to foster the best listening experience in dedicated listening rooms, with the help of the most up to date acoustic techniques developed at the Laboratory of Electromagnetics and Acoustics. This question yielded to the construction of two breakthrough acoustic prototypes, the SoundRelief and the SoundDots, that should lead to new concepts of multipurpose listening spaces. In parallel, the advanced acoustic signal processing techniques developed at the laboratory were applied to the automatic recognition of sound events in the whole recording flux, that will help the digitization process, through features such as applause/speech/music detection, metadata identification or time-code assignment

On Room Mode Analysis for Classifying Indoor Events Using Loudspeaker and AI

Room modes are known to alter the low frequency response of electrodynamic loudspeakers and are one of the biggest obstacles to accurate sound reproduction in listening rooms. The characteristics of the room may therefore influence the response of the loudspeaker at low frequencies due of the strong modal acoustic coupling. In this presentation, we show how to take advantage of this interaction to detect changes occurring in the room from the loudspeaker impedance. We present a practical methodology that combines electroacoustics and AI to track changes in the modal frequency response of the room related to the presence of people, the opening of doors and windows or a temperature shift. The performance and limitations of the concept are illustrated using calculated data and measurements taken in a real room

STUDY OF AN OCTAHEDRAL ANTENNA FOR BOTH SOUND PRESSURE LEVEL ESTIMATION AND 3D LOCALIZATION OF MULTIPLE SOURCES

This study deals with localization and characterization of multiple sources by mean of an octahedral array processing. We show how correlations enable to both localize more sources that the number of sensors and estimate their sound level pressure simultaneously. The theoretical basis of the algorithm, based on analytical geometry and time differences of arrival, is explained for the broad-band sources in the far field and experimented in anechoic conditions. The localization error due to uncertainty of some physical measurements and numerical recording is studied. Results and limitations of the proposed method are discussed