Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees

This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and projection onto the Euclidean distance space. This approach confines the recovered matrix to the EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of the calibration performance are obtained considering the random and locally structured missing entries as well as the measurement noise on the known distances. This study elucidates the links between the calibration error and the number of microphones along with the noise level and the ratio of missing distances. Thorough experiments on real data recordings and simulated setups are conducted to demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.Comment: In Press, available online, August 1, 2014. http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal Processing, 201

Localization of sound sources : a systematic review

Sound localization is a vast field of research and advancement which is used in many useful applications to facilitate communication, radars, medical aid, and speech enhancement to but name a few. Many different methods are presented in recent times in this field to gain benefits. Various types of microphone arrays serve the purpose of sensing the incoming sound. This paper presents an overview of the importance of using sound localization in different applications along with the use and limitations of ad-hoc microphones over other microphones. In order to overcome these limitations certain approaches are also presented. Detailed explanation of some of the existing methods that are used for sound localization using microphone arrays in the recent literature is given. Existing methods are studied in a comparative fashion along with the factors that influence the choice of one method over the others. This review is done in order to form a basis for choosing the best fit method for our use

IMPACT OF MICROPHONE POSITIONAL ERRORS ON SPEECH INTELLIGIBILITY

The speech of a person speaking in a noisy environment can be enhanced through electronic beamforming using spatially distributed microphones. As this approach demands precise information about the microphone locations, its application is limited in places where microphones must be placed quickly or changed on a regular basis. Highly precise calibration or measurement process can be tedious and time consuming. In order to understand tolerable limits on the calibration process, the impact of microphone position error on the intelligibility is examined. Analytical expressions are derived by modeling the microphone position errors as a zero mean uniform distribution. Experiments and simulations were performed to show relationships between precision of the microphone location measurement and loss in intelligibility. A variety of microphone array configurations and distracting sources (other interfering speech and white noise) are considered. For speech near the threshold of intelligibility, the results show that microphone position errors with standard deviations less than 1.5cm can limit losses in intelligibility to within 10% of the maximum (perfect microphone placement) for all the microphone distributions examined. Of different array distributions experimented, the linear array tends to be more vulnerable whereas the non-uniform 3D array showed a robust performance to positional errors

CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed

Locating and extracting acoustic and neural signals

This dissertation presents innovate methodologies for locating, extracting, and separating multiple incoherent sound sources in three-dimensional (3D) space; and applications of the time reversal (TR) algorithm to pinpoint the hyper active neural activities inside the brain auditory structure that are correlated to the tinnitus pathology. Specifically, an acoustic modeling based method is developed for locating arbitrary and incoherent sound sources in 3D space in real time by using a minimal number of microphones, and the Point Source Separation (PSS) method is developed for extracting target signals from directly measured mixed signals. Combining these two approaches leads to a novel technology known as Blind Sources Localization and Separation (BSLS) that enables one to locate multiple incoherent sound signals in 3D space and separate original individual sources simultaneously, based on the directly measured mixed signals. These technologies have been validated through numerical simulations and experiments conducted in various non-ideal environments where there are non-negligible, unspecified sound reflections and reverberation as well as interferences from random background noise. Another innovation presented in this dissertation is concerned with applications of the TR algorithm to pinpoint the exact locations of hyper-active neurons in the brain auditory structure that are directly correlated to the tinnitus perception. Benchmark tests conducted on normal rats have confirmed the localization results provided by the TR algorithm. Results demonstrate that the spatial resolution of this source localization can be as high as the micrometer level. This high precision localization may lead to a paradigm shift in tinnitus diagnosis, which may in turn produce a more cost-effective treatment for tinnitus than any of the existing ones