Simultaneous asynchronous microphone array calibration and sound source localisation

© 2015 IEEE. In this paper, an approach for sound source localisation and calibration of an asynchronous microphone array is proposed to be solved simultaneously. A graph-based Simultaneous Localisation and Mapping (SLAM) method is used for this purpose. Traditional sound source localisation using a microphone array has two main requirements. Firstly, geometrical information of microphone array is needed. Secondly, a multichannel analog-to-digital converter is required to obtain synchronous readings of the audio signal. Recent works aim at releasing these two requirements by estimating the time offset between each pair of microphones. However, it was assumed that the clock timing in each microphone sound card is exactly the same, which requires the clocks in the sound cards to be identically manufactured. A methodology is hereby proposed to calibrate an asynchronous microphone array using a graph-based optimisation method borrowed from the SLAM literature, effectively estimating the array geometry, time offset and clock difference/drift rate of each microphone together with the sound source locations. Simulation and experimental results are presented, which prove the effectiveness of the proposed methodology in achieving accurate estimates of the microphone array characteristics needed to be used on realistic settings with asynchronous sound devices

Real-time sound source localisation for target tracking applications using an asynchronous microphone array

© 2015 IEEE. This paper presents a strategy for sound source localisation using an asynchronous microphone array. The proposed method is suitable for target tracking applications, in which the sound source with a known frequency is attached to the target. Conventional microphone array technologies require a multi-channel A/D converter for inter-microphone synchronization making the technology relatively expensive. In this work, the requirement of synchronization between channels is relaxed by adding an external reference audio signal. The only assumption is that the frequencies of the reference signal and the sound source attached to the target are fixed and known beforehand. By exploiting the information provided by the known reference signal, the Direction Of Arrival (DOA) of target sound source can be calculated in real-time. The key idea of the algorithm is to use the reference source to 'pseudo-align' the audio signals from different channels. Once the channels are 'pseudo-aligned', a dedicated DOA estimation method based on Time Difference Of Arrival (TDOA) can be employed to find the relative bearing information between the target sound source and microphone array. Due to the narrow band of frequency of target sound source, the proposed approach is proven to be robust to low signals-to-noise ratios. Comprehensive simulations and experimental results are presented to show the validity of the algorithm

Robotic sound source mapping using microphone arrays

University of Technology Sydney. Faculty of Engineering and Information Technology.The auditory system constitutes a significant perceptual input for humans and animals. While it is legitimate to say that it ranks behind other senses such as vision or haptics whose understanding has experienced significant advances in the context of computational intelligence and robotics, it is intuitive to assume that service and field robotic systems working closely with humans would benefit from incorporating compelling sound analysis capabilities in the pursuit of accomplishing human-robot collaborative tasks. Within the broad area of robotic audition, one of the most relevant research topics has been identifying and locating multiple sound sources that may be present in the vicinity of the robot at an instant in time. Robotic systems equipped with such ability would gain the faculty to better monitor acoustic events such as a conversation, a ringing alarm or a call for help, for example in a search and rescue scenario, effectively responding to people's needs in a more natural way. Mapping stationary sound sources using a robot equipped with an on-board microphone array is thus the main focus of this thesis

Audio Fingerprinting for Multi-Device Self-Localization

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Audio-based localization for ubiquitous sensor networks

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 97-101).This research presents novel techniques for acoustic-source location for both actively triggered, and passively detected signals using pervasive, distributed networks of devices, and investigates the combination of existing resources available in personal electronics to build a digital sensing 'commons'. By connecting personal resources with those of the people nearby, tasks can be achieved, through distributed placement and statistical improvement, that a single device could not do alone. The utility and benefits of spatio-temporal acoustic sensing are presented, in the context of ubiquitous computing and machine listening history. An active audio self-localisation algorithm is described which is effective in distributed sensor networks even if only coarse temporal synchronisation can be established. Pseudo-noise 'chirps' are emitted and recorded at each of the nodes. Pair-wise distances are calculated by comparing the difference in the audio delays between the peaks measured in each recording. By removing dependence on fine grained temporal synchronisation it is hoped that this technique can be used concurrently across a wide range of devices to better leverage the existing audio sensing resources that surround us.(cont.) A passive acoustic source location estimation method is then derived which is suited to the microphone resources of network-connected heterogeneous devices containing asynchronous processors and uncalibrated sensors. Under these constraints position coordinates must be simultaneously determined for pairs of sounds and recorded at each microphone to form a chain of acoustic events. It is shown that an iterative, numerical least-squares estimator can be used. Initial position estimates of the source pair can be first found from the previous estimate in the chain and a closed-form least squares approach, improving the convergence rate of the second step. Implementations of these methods using the Smart Architectural Surfaces development platform are described and assessed. The viability of the active ranging technique is further demonstrated in a mixed-device ad-hoc sensor network case using existing off-the-shelf technology. Finally, drawing on human-centric onset detection as a means of discovering suitable sound features, to be passed between nodes for comparison, the extension of the source location algorithm beyond the use of pseudo-noise test sounds to enable the location of extraneous noises and acoustic streams is discussed for further study.Benjamin Christopher Dalton.S.M

Audio localization for mobile robots

The department of the University for which I worked is developing a project based on the interaction with robots in the environment. My work was to define an audio system for the robot. This audio system that I have to realize consists on a mobile head which is able to follow the sound in its environment. This subject was treated as a research problem, with the liberty to find and develop different solutions and make them evolve in the chosen way.Preprin

Incorporation of acoustic sensors in the regulation of a mobile robot

This article introduces the incorporation of acoustic sensors for the localization of a mobile robot. The robot is considered as a sound source and its position is located applying a Time Delay of Arrival (TDOA) method. Since the accuracy of this method varies with the microphone array, a navigation acoustic map that indicates the location errors is built. This map also provides the robot with navigation trajectories point-to-point and the control is capable to drive the robot through these trajectories to a desired configuration. The proposed localization method is thoroughly tested using both a 900 Hz square signal and the natural sound of the robot, which is driven near the desired point with an average error of 0:067 m.This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 01/01/2019, available online: http://www.tandfonline.com/10.1080/01691864.2019.1573703.”Peer ReviewedPostprint (author's final draft