Sound positioning using a small-scale linear microphone array (original) (raw)
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Two-stage Localisation Scheme Using a Small-scale Linear Microphone Array for Indoor Environments
Journal of Navigation, 2015
The small-scale linear microphone arrays that are widely found in smartphones could be used to locate a sound source for indoor environments. After the Time Differences Of Arrival (TDOAs) in microphone pairs are estimated, a TDOA-based hybrid localisation scheme is proposed for a small-scale linear microphone array. The scheme contains two stages: the initialisation stage using the Levenberg-Marquardt (LM) algorithm, and the refining solution stage using the Weighted Least-Square (WLS) algorithm or the Multi-Dimensional Scaling-based (MDS) algorithm. Simulations and field tests show that the proposed indoor localisation scheme outperforms the existing schemes, and it can achieve an average error of 0ยท32 metres in an 8 m by 5 m area.
Microphone array positioning technique with Euclidean distance geometry
Applied Acoustics, 2020
Source localization and quantification by an acoustic array of microphones depend to a great extent on an accurate knowledge of the antenna position towards the radiating device. The present work details a methodology to determine the location of the microphones in relation to an object of study, starting from its geometric shape and that of the array, in order to reproduce an experimental configuration in any retro-propagating method. A set of reference sources are placed on several prominent locations of the device to estimate the times of flight (ToF) (and distances) between them and the microphones, connecting the array and the object together. The overall geometric configuration is thus defined by an Euclidean Distance Matrix (EDM), which is basically the matrix of squared distances between points. First, MultiDimensional Unfolding (MDU) technique is used to reconstruct the point set from distances. Second, this point set is then aligned with the device, using reference sources as anchor nodes. This orthogonal Procustes problem is solved by the Kabsch algorithm to obtain the optimal rotation and translation matrices between the coordinate system of the array and that of the object of study. The methodology is detailed, validated first by a numerical simulation of a typical experimental setup. An experimental campaign is finally carried out to assess the robustness of the method in a typical test case.
Microphone arrays application in three-dimensional sound source localisation
International Journal of Intelligent Information and Database Systems, 2012
This article describes the problems of use of spatial microphone arrays in sound source location. Questions concerning modelling of planar (2-D) and spatial (3-D) directional characteristics of microphone arrays are discussed. Typical algorithms used in SSL system are also described. The paper describes options to use a single point surround microphone to determine a direction of the sound source localisation. A 'soundfield' microphone with four transducers (capsules) characterised by cardioid directivity pattern. A unique mechanical design of the transducer results in its omnidirectional directivity pattern. The microphone enables 3-D sound acquisition in so called A-format. Upon further processing of signal it is possible to determine, e.g., a direction of a sound source within space. The conducted experiments prove that a simple calculation algorithm is in particular feasible for a real time operation, and application of the soundfield microphone significantly simplifies mechanical design of the SSL system.
The Effect of Calibration Errors on Source Localization with Microphone Arrays
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Source localization employing time-differences-of-arrival has been employed for many applications. The accuracy of source localization is limited by the errors in the time differences of arrival estimation as well as microphone position calibration errors. Because a microphone position error will affect multiple time differences of arrival, correlation between these quantities will be introduced. This work presents a new mathematical framework
Sound Localization for Ad-Hoc Microphone Arrays
Energies, 2021
Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Prev...
Time-domain GCC-phat sound source localization for small microphone arrays
2012
In sound enhancement, wireless acoustic sensor networks (WASN) are gaining popularity. This is due to recent progress on both the hard-and software side. Nowadays affordable, miniaturized and powerful signal processing hardware is available to build the individual sensor nodes. Combined with novel distributed software, allowing to share computational load among all network nodes, WASNs are able to handle complex signal processing tasks. One such task is the enhancement of speech recorded from a distance. A subtask in the enhancement process might be the estimation of the speakers position. This paper focusses on implementing an accurate Sound Source Localizer (SSL) for estimating the position of a sound source on a digital signal processor (to be included in an individual WASN node) using as less CPU resources as possible. One of the least complex algorithms for SSL is a simple correlation, implemented in the frequencydomain for efficiency, combined with a frequency bin weighing for robustness. Together called Generalized Cross Correlation (GCC). One popular weighing called GCC PHAse Transform (GCC-PHAT) will be handled. In this paper it is explained that for small microphone arrays, as mounted on a single acoustic node of a WASN, this frequency-domain implementation is inferior to its time-domain alternative in terms of algorithmic complexity. Therefore a time-domain PHAT equivalent will be described. Both implementations are compared in terms of complexity (clock cycles needed on a Texas Instruments C5515 DSP) and obtained results, showing a complexity gain with a factor of 146, with hardly any loss in localization accuracy.
Accuracy Study of a Real-Time Hybrid Sound Source Localization Algorithm
2013
Sound source localization in real time can be employed in numerous applications such as filtering, beamforming, security system integration, etc. Algorithms employed in this field require not only fast processing speed but also enough accuracy to properly cope with the application requirements. This work presents accuracy benchmarks of a hybrid approach previously proposed, which is based on the Generalized Cross Correlation (GCC), and the Delay and Sum beamforming (DSB). Tests were performed considering a linear microphone array simulated in MATLAB. Analysis through variations in array size, number of microphones, spacing and other characteristics, were included. Results obtained show that the proposed algorithm is as good as the DSB under some conditions that can be easily met.
Robust Microphone Placement for Source Localization from Noisy Distance Measurements
We propose a novel algorithm to design an optimum array geometry for source localization inside an enclosure. We assume a square-law decay propagation model for the sound acquisition so that the additive noise on the measured source-microphone distances is proportional to the distances regardless of the noise distribution. We formulate the source localization as an instance of the "Generalized Trust Region Subproblem" (GTRS) whose solution gives the location of the source. We show that by suitable selection of the microphone locations, one can tremendously decrease the noise-sensitivity of the resulting solution. In particular, by minimizing the noise-sensitivity of the source location in terms of sensor positions, we find the optimal noise-robust array geometry for the enclosure. Simulation results are provided to show the efficiency of the proposed algorithm.
Robotics and Autonomous Systems, 2019
Human-robot interaction in natural settings requires filtering out the different sources of sounds from the environment. Such ability usually involves the use of microphone arrays to localize, track and separate sound sources online. Multimicrophone signal processing techniques can improve robustness to noise but the processing cost increases with the number of microphones used, limiting response time and widespread use on different types of mobile robots. Since sound source localization methods are the most expensive in terms of computing resources as they involve scanning a large 3D space, minimizing the amount of computations required would facilitate their implementation and use on robots. The robot's shape also brings constraints on the microphone array geometry and configurations. In addition, sound source localization methods usually return noisy features that need to be smoothed and filtered by tracking the sound sources. This paper presents a novel sound source localization method, called SRP-PHAT-HSDA, that scans space with coarse and fine resolution grids to reduce the number of memory lookups. A microphone directivity model is used to reduce the number of directions to scan and ignore non significant pairs of microphones. A configuration method is also introduced to automatically set parameters that are normally empirically tuned according to the shape of the microphone array. For sound source tracking, this paper presents a modified 3D Kalman (M3K) method capable of simultaneously tracking in 3D the directions of sound sources. Using a 16-microphone array and low cost hardware, results show that SRP-PHAT-HSDA and M3K perform at least as well as other sound source localization and tracking methods while using up to 4 and 30 times less computing resources respectively.