Acoustic Source Localization Algorithm Research Articles

Acoustic Source Localization in a Reverberant Environment Based on Sound Field Morphological Component Analysis and Alternating Direction Method of Multipliers

The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SF-MCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green’s function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation ( $T_{20}=174.30\,\,\text {ms}$ ). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise.

A classification approach to the characterization of seabed geoacoustic profiles via deep learning

Acoustic source localization algorithms in the shallow ocean require information about the ocean seabed. While past approaches have characterized seabed geoacoustic profiles via continuous physical parameters, a classification approach approximates the distribution of seabed types as a discrete set of seabed classes. To exclude acoustically indistinguishable seabeds from the discrete classes, the acoustic separation between two seabeds may be evaluated by the Pearson correlation between their characteristic transmission loss curves. This process yields a subset of discrete seabed classes. To distinguish these classes, we train a convolutional neural network classifier on the pressure time-series of SUS charges, which are simulated using the geoacoustic profiles of the seabed classes. This network successfully predicts seabed class, when tested on a holdout set where the sound speed profiles of the ocean are adequately similar to those in the training set. An acoustic separation measure between sound speed profiles, analogous to the one for seabeds, directly relates to the generalization error of the network. [Work supported by the Office of Naval Research Contract No. N00014-16-C-3065 and by the NSF REU program, Grant No. 1757998.]

Acoustic Source Localization Based on Geometric Projection in Reverberant and Noisy Environments

Acoustic source localization (ASL) is a fundamental yet still challenging signal processing problem in sound acquisition, speech communication, and human–machine interfaces. Many ASL algorithms have been developed, such as the steered response power (SRP), the SRP-phase transform, the minimum variance distortionless response, the multiple signal classification (MUSIC), the householder transform-based methods, to name but a few. Most of those algorithms require hundreds or even thousands of snapshots to produce one reliable estimate, which make them difficult to track moving sources. Moreover, not much efforts have been reported in the literature to show the intrinsic relationships among those methods. This paper deals with the ASL problem with its focal point placed on how to achieve ASL with a short frame of acoustic signal (corresponding to a single snapshot in the frequency domain). It reformulates the ASL problem from the perspective of geometric projection. Four types of power functions are proposed, leading to several different algorithms for ASL. By analyzing those power functions, we show the equivalence between the popularly used conventional algorithms and our methods, which provides some new insights into the conventional algorithms. The relationships among different algorithms are discussed, which make it easy to comprehend the pros and cons of each of those methods. Experiments in real acoustic environments corroborate the theoretical analysis, which in turn justifies the contribution of this paper.

A Research on Acoustic Emission Signal Source Localization Algorithm

A Research on Acoustic Emission Signal Source Localization Algorithm

Localization of adventitious respiratory sounds.

In a recent publication by Henry and Royston [J. Acoust. Soc. Am. 142, 1774-1783 (2017)], an algorithm was introduced to calculate the acoustic response to externally introduced and endogenous respiratory sounds within a realistic, patient-specific subglottal airway tree. This work is extended using an efficient numerical boundary element (BE) approach to calculate the resulting radiated sound field from the airway tree into the lung parenchyma taking into account the surrounding chest wall. Within the BE model of the left lung parenchyma, comprised of more than 6000 triangular surface elements, more than 30 000 monopoles are used to approximate complex airway-originated acoustic sources. The chest wall is modeled as a boundary condition on the parenchymal surface. Several cases were simulated, including a bronchoconstricted lung that had an internal acoustic source introduced in a bronchiole, approximating a wheeze. An acoustic source localization algorithm coupled to the BE model estimated the wheeze source location to within a few millimeters based solely on the acoustic field at the surface. Improved noninvasive means of locating adventitious respiratory sounds may enhance an understanding of acoustic changes correlated to pathology, and potentially provide improved noninvasive tools for the diagnosis of pulmonary diseases that uniquely alter acoustics.

Research on near field acoustic source localization algorithm based on arbitrary planar vector array

In order to solve the problem of near acoustic target location, vector hydrophone array with arbitrary formation is proposed to locate near field acoustic source. In this paper, firstly, the near field measurement model of vector hydrophone array is derived detailed. Then, introduces the conventional beamforming and MVDR algorithm based on vector hydrophone array. Finally, the performance of the algorithm is verified by simulation. The results show that the vector MVDR has higher target resolution, and the error decreases with the increase of SNR. It is more suitable for underwater near field regional warning.

Deconvolution-based acoustic source localization and separation algorithms

In this paper, localization and separation of acoustic sources are examined. Depending on the number of sources in relation to the array channels, the problem is investigated in terms of underdetermined and overdetermined configurations. In the underdetermined configuration, virtual monopole sources are assumed in uniformly spaced angles. The problem is then formulated into compressive sampling (CS) problem which can be solved by using the linearly constrained -norm convex (CVX) optimization. The solution yields the directions of real sources and the source signal spectrum, which enables localization and reconstruction of sources at one shot. In the underdetermined configuration, source localization and signal separation is carried out in two steps. First, the directions of arrival (DOA) are estimated with Minimum Variance Distortionless Response (MVDR) or Multiple Signal Classification (MUSIC). Next, Tikhonov regularization (TIKR) is utilized to recover the source spectrum. In the localization problem for both configurations, Neyman-Pearson detector is employed to determine thresholds for source detection. Numerical and experimental results show that the proposed methods produce improved speech quality in terms of mean opinion score (MOS) in perceptual evaluation of speech quality (PESQ) test.

Distributed, Robust Acoustic Source Localization in a Wireless Sensor Network

A distributed, robust source location estimation method using acoustic signatures in a wireless sensor network (WSN) is presented. A contaminated Gaussian (CG) noise model is proposed to characterize the impulsive, non-Gaussian nature of acoustic background noise observed in some real-world WSNs. A bi-square M-estimate approach then is applied to provide robust estimation of acoustic source locations in the presence of outlier. Moreover, a Consensus based Distributed Robust Acoustic Source Localization (C-DRASL) algorithm is proposed. With C-DRASL, individual sensor nodes will solve for the bi-square M-estimate of the source location locally using a lightweight Iterative Nonlinear Reweighted Least Square (INRLS) algorithm. These local estimates then will be exchanged among nearest neighboring nodes via one-hop wireless channels. Finally, at each node, a robust consensus algorithm will aggregate the local estimates of neighboring nodes iteratively and converge to a unified global estimate on the source location. The effectiveness and robustness of C-DRASL are clearly demonstrated through extensive simulation results.

Acoustic Source Localization and Tracking Using Track Before Detect

Particle Filter-based Acoustic Source Localization algorithms attempt to track the position of a sound source - one or more people speaking in a room - based on the current data from a microphone array as well as all previous data up to that point. This paper first discusses some of the inherent behavioral traits of the steered beamformer localization function. Using conclusions drawn from that study, a multitarget methodology for acoustic source tracking based on the Track Before Detect (TBD) framework is introduced. The algorithm also implicitly evaluates source activity using a variable appended to the state vector. Using the TBD methodology avoids the need to identify a set of source measurements and also allows for a vast increase in the number of particles used for a comparitive computational load which results in increased tracking stability in challenging recording environments. An evaluation of tracking performance is given using a set of real speech recordings with two simultaneously active speech sources.

Locating Acoustic Events Based on Large-Scale Sensor Networks

Research on acoustic source localization is actively being conducted to enhance accuracy and coverage. However, the performance is inherently limited due to the use of expensive sensor nodes and inefficient communication methods. This paper proposes an acoustic source localization algorithm for a large area that uses low-cost sensor nodes. The proposed mechanism efficiently handles multiple acoustic sources by removing false-positive errors that arise from the different propagation ranges of radio and sound. Extensive outdoor experiments with real hardware validated that the proposed mechanism could localize four acoustic sources within a 3 m error in a 60 m by 60 m area, where conventional systems could hardly achieve similar performance.

Decentralized acoustic source localization in a distributed sensor network

The purpose of this work is to investigate acoustic source localization algorithms suitable for use with distributed sensor networks. Traditional sensor networks employ a central controller; however centralized data processing in large-scale sensor networks is not always desirable because of the excessive communication and computational complexity it requires. Therefore, fully distributed localization algorithms are considered. The most important aspect of these algorithms is that they be scalable for use in large sensor networks. The scalability is achieved by forming sensor nodes into groups which collaborate to locate sources. Source locations are determined from time of arrival (TOA) information of the acoustic wave front. Two source location solution methods are used: least squares (LS) and Tikhonov regularized inversion (RI). Experimental results validate the accuracy of a distributed localization approach, and the effectiveness of the LS and RI methods are compared. Additionally, important parameters of the distributed localization algorithm are considered.

A decentralized algorithm for acoustic localization using a distributed sensor network

An acoustic source localization algorithm has been developed for use with large-scale sensor networks using a decentralized computing approach. This algorithm, based on a time delay of arrival (TDOA) method, uses information from the minimum number of sensors necessary for an exactly determined solution. Since the algorithm is designed to run on computational devices with limited memory and speed, the complexity of the computations has been intentionally limited. The sensor network consists of an array of battery-operated COTS Ethernet-ready embedded systems with an integrated microphone as a sensor. All solutions are calculated as distinct values, and the same TDOA method used for solution is applied for ranking the accuracy of an individual solution. Repeated for all combinations of sensor nodes, solutions with accuracy equivalent to complex array calculations are obtainable. Effects of sensor placement uncertainty and multipath propagation are quantified and analyzed, and a comparison to results obtained in the field with a large array with a centralized computing capability using a complex, memory intensive algorithm is included.

Particle filtering algorithms for tracking an acoustic source in a reverberant environment

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle filtering. In this paper we formulate a general framework for tracking an acoustic source using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 s). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.

Algorithms for acoustic localization based on microphone array in service robotics

This paper deals with the development of acoustic source localization algorithms for service robots working in real conditions. One of the main utilizations of these algorithms in a mobile robot is that the robot can localize a human operator and eventually interact with him/herself by means of verbal commands. The location of a speaking operator is detected with a microphone array based algorithm; localization information is passed to a navigation module which sets up a navigation mission using knowledge of the environment map. In fact, the system we have developed aims at integrating acoustic, odometric and collision sensors with the mobile robot control architecture. Good performance with real acoustic data have been obtained using neural network approach with spectral subtraction and a noise robust voice activity detector. The experiments show that the average absolute localization error is about 40 cm at 0 dB and about 10 cm at 10 dB of SNR for the named localization. Experimental results describing mobile robot performance in a talker following task are reported.