SPEECH ACQUISITION IN NOISY ENVIRONMENTS USING DUAL MICROPHONE ARRAYS

Subject of Research.The paper deals with the practical aspects of distant speech acquisition in complex noisy environments using dual microphone arrays (MA2). The non-adaptive frequency-domain algorithms are described. The theory of MA2 is well developed so far, but the application of MA2 in specific conditions requires special consideration. The scenarios of point coherent interference and spatially distributed noise are studied.Methods. The comparison of differential algorithms and delay-and-sum algorithm is presented. The main properties of MA2 with summation algorithm and differential algorithms are researched on the basis of analytical models. Algorithms were tested on anechoic chamber recordings. The efficiency of the algorithms has been studied on recordings made near the street with intensive traffic. Main Results. The experimental results show the advantage of differential algorithms over delay-and-sum algorithm of both noise and interference suppression. For different variants of differential algorithms, street noise suppression about 10-12 dB is achieved. An additional advantage of differential algorithms is the possibility of null forming in the direction of a point source of interference. Practical Relevance. The results obtained may be used in hands free communication devices, camera equipment, portable recording devices, in acoustic monitoring systems. The results of the analysis of MA2 algorithms can also be used in the development of compact microphone arrays, as well as microphone arrays with a large number of elements

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