Sound Source Localization Method Research Articles

Comparison of the sound source localization methods appropriate for a compact microphone array

Comparison of the sound source localization methods appropriate for a compact microphone array

Sound source localization method by using a single moving microphone and inner product calculation

In this paper, a microphone is arranged to move along a line at a low speed while collecting sound pressure signals, then the mathematical relation is researched to locate the source and the localization processes are as follows. The collected continuous-time signals are segmented and each segment is transferred to the frequency domain by integral transformation. As a result, according to the Riemann Lebesgue theorem, the interference from the negative frequency term is eliminated, a correlation relationship between the real sound source and virtual sound source (a hypothetical sound source) is set up. By using the formed correlation, the position of the sound source is located by founding the maximum of the inner product module through optimization. Analyzing the details of the segmentation, it is found that the correlation will be enhanced when segment time length is integer multiples of signal's half period, and it benefits the localization accuracy. This method is also extended to more complicated multi-point source identification. Simulation examples and experiments show that the proposed method can locate the sound source. This method uses only one microphone, there is no demanding phase matching and calibration work like the of a microphone array, the cost of use is low.

Three-Dimensional Sound Field Reconstruction and Sound Power Estimation by Stereo Vision and Beamforming Technology

The size of the sound field reconstruction area has an important influence on the beamforming sound source localization method and determines the speed of reconstruction. To reduce the sound field reconstruction area, stereo vision technology is introduced to continuously obtain the three-dimensional surface of the target and reconstruct the sound field on it. The fusion method can quickly locate the three-dimensional position of the sound source, and the computational complexity of this method is mathematically analyzed. The sound power level can be estimated dynamically by the sound intensity scaling method based on beamforming and the depth information of the sound source. Experimental results in a hemi-anechoic chamber show that this method can quickly identify the three-dimensional position of the moving source. When the depth of the moving sound source changes, the estimated sound power is more stable than the sound pressure on the microphone.

Iterative Modified SRP-PHAT with Adaptive Search Space for Acoustic Source Localization

The Steered Response Power with Phase Transform (SRP-PHAT) is one of the leading methods for sound source localization because of its proven robustness in a noisy and reverberant environment. However, for the higher accuracy, the maximum power location needs to be searched over a dense grid, making the computational cost a real issue for large-scale microphone arrays. Modified SRP-PHAT introduced an effective strategy that computes the SRP functional at each discrete location, also considering the volume surrounding it. Subsequently, it explores the entire search space and achieves improved robustness over a coarse spatial grid. To accomplish higher spatial resolution with minimal computations, the Modified SRP-PHAT was further extended by employing an iterative grid decomposition search strategy. However, in challenging environments, the performance of the method deteriorates considerably with the iterative approach. In this paper, we suggest furtherance to iterative modified SRP-PHAT by employing adaptive search space in each iteration. The performance of the proposed method is extensively tested and compared to the state-of-the-art techniques on a simulated dataset and SMARD database to validate its reliability. The results exhibited that the proposed method achieves higher accuracy with only a marginal increase in computation expense than that of iterative modified SRP-PHAT.

Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments

A multiple sound source localization and counting method based on an angular spectrum is proposed in this paper. Local signal-to-noise ratio tracking, onset detection, and a coherence test are introduced to filter the generalized cross-correlation angular spectrum in the time-frequency domain for multiple sound source localization and counting in noisy and reverberant environments. Then, dual-width matching pursuit is introduced to replace peak search as the method of localization and counting. A comprehensive comparison of two statistical indicators, mean precision and mean absolute estimated error, indicates that the proposed localization and counting algorithm using both the filtered angular spectrum and dual-width matching pursuit method is more robust and accurate than the classic counterpart, especially in environments with low signal-to-noise ratio, strong reverberation, and abundant sound sources.

Gerçek Ortamlarda Artımlı Öğrenme ile Gerçek Zamanlı İşitsel Sahne Analizi

Continual learning for scene analysis is a continuous process to incrementally learn distinct events, actions, and even noise models from past experiences using different sensory modalities. In this paper, an Auditory Scene Analysis (ASA) approach based on a continual learning system is developed to incrementally learn the acoustic events in a dynamically-changing domestic environment. The events being salient sound sources are localized by a Sound Source Localization (SSL) method to robustly process the signals of the localized sound source in the domestic scene where multiple sources can co-exist. For real-time ASA, audio patterns are segmented from the acoustic signal stream of the localized source for extraction of the audio features, and construction of a feature set for each pattern. The continual learning is employed via a time-series algorithm, Hidden Markov Model (HMM), on these feature sets from acoustic signals stemming from the sources. The learning process is investigated by conducting a variety of experiments to evaluate the performance of Unknown Event Detection (UED), Acoustic Event Recognition (AER), and continual learning using a Hierarchical HMM algorithm. The Hierarchical HMM consists of two layers: 1) a lower layer in which AER is performed using an HMM for each event and the event-wise likelihood thresholds; and 2) an upper layer in which UED is achieved by one HMM with a suspicion threshold through the audio features with their proto symbols stemming from the lower layer HMMs. We verified the effectiveness of the proposed system capable of continual learning, AER and UED in terms of False-Positive Rates, True-Positive Rates, recognition accuracy and computational time to meet the demands in a learning task of multiple events in real-time. The effectiveness of the AER system has been verified with high accuracy, and a short retraining time in real-time ASA having nine different sounds.

DATASET FOR EVALUATION OF THE PERFORMANCE OF THE METHODS OF SOUND SOURCE LOCALIZATION ALGORITHMS USING TETRAHEDRAL MICROPHONE ARRAYS

For the development and evaluation of a sound source localization and separation methods, a concise audio dataset with complete geometrical information about the room, the positions of the sound sources, and the array of microphones is needed. Computer simulation of such audio and geometrical data often relies on simplifications and are sufficiently accurate only for a specific set of conditions. It is generally desired to evaluate algorithms on real-world data. For a three-dimensional sound source localization or direction of arrival estimation, a non-coplanar microphone array is needed.Simplest and most general type of non-coplanar array is a tetrahedral array. There is a lack of openly accessible realworld audio datasets obtained using such arrays. We present an audio dataset for the evaluation of sound source localization algorithms, which involve tetrahedral microphone arrays. The dataset is complete with the geometrical information of the room, the positions of the sound sources and the microphone array. Array audio data was captured for two tetrahedral microphone arrays with different distances between microphones and one or two active sound sources. The dataset is suitable for speech recognition and direction-of-arrival estimation, as the signals used for sound sources were speech signals.

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramer-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source.

Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios.

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

Improving Pedestrian Safety in Cities Using Intelligent Wearable Systems

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this paper, we build a wearable system that uses multichannel audio sensors embedded in a headset to help detect and locate cars from their honks, engine, and tire noises, and warn pedestrians of imminent dangers of approaching cars. We demonstrate that using a segmented architecture consisting of headset-mounted audio sensors, a front-end hardware platform that performs signal processing and feature extraction, and machine learning-based classification on a smartphone, we are able to provide early danger detection in real time, from up to 60 m away, and alert the user with low latency and high accuracy. To further reduce power consumption of the battery-powered wearable headset, we implement a custom-designed integrated circuit that is able to compute delays between multiple channels of audio with nW power consumption. A regression-based method for sound source localization, angle via polygonal regression, is proposed and used in combination with the IC to improve the granularity and robustness of localization.

A computationally efficient blind source separation for hearing aid applications and its real-time implementation on smartphone

Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many real-time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone to separate speech and noise in real-world scenarios for hearing-aid applications. The experimental results with objective and subjective tests reveal the usefulness of the developed method for real-world applications.

Multiple Sound Source Counting and Localization Based on TF-Wise Spatial Spectrum Clustering

This paper addresses the problem of multiple sound source counting and localization in adverse acoustic environments, using microphone array recordings. The proposed time-frequency TF wise spatial spectrum clustering based method contains two stages. First, given the received sensor signals, the spatial correlation matrix is computed and denoised in the TF domain. The TF-wise spatial spectrum is estimated based on the signal subspace information, and further enhanced by an exponential transform, which can increase the reliability of the source presence possibility reflected by spatial spectrum. Second, to jointly count and localize sound sources, the enhanced TF-wise spatial spectra are divided into several clusters with each cluster corresponding to one source. Sources are successively detected by searching the significant peaks of the remaining global spatial spectrum, which is formed using unassigned spatial spectra. After each new source detection, spatial spectra are reassigned to detected sources according to the dominance association between them. The interaction between sources is reduced by iteratively performing new source detection and spatial spectrum assignment. Experiments on both simulated data and real-world data demonstrate the superiority of the proposed method for multiple sound source counting and localization in the environment with different levels of noise and reverberation.

Sound Source Localization Using Head-Related Transfer Functions and Weighted Error Function

This study proposes a sound source localization method using binaural input signals. The method is based on the head-related transfer function (HRTF) database and the interaural transfer function (ITF) obtained from two measured input signals. An algorithm to reduce the effect of background noise on the localization performance in a noisy environment was adopted in the proposed localization method. Weighted error functions (WEFs), defined using the ITF and the ratio of HRTFs for two ears, were used with a special frequency weighting function derived to reduce the effect of noise and to render the WEF a physical meaning. Computer simulations confirmed that the weighting function can effectively reduce the effect of background noise on the localization performance even if the noise power is very high. Localization tests in an actual room confirmed that both the azimuth and elevation angles of sound source can be estimated simultaneously with high accuracy. In particular, the front-back and updown confusions, which are critical limitations for conventional localization methods, could be resolved using two input signals.

High-precision sound source localization method based on successive approximation in an inhomogeneous sound velocity field

In an inhomogeneous sound velocity field, the conventional time of flight (ToF) method cannot derive an exact distance directly. Therefore, a high-precision sound source localization method is required to address this difficult problem. In this study, we propose a novel high-accuracy successive approximation sound source localization method (SALM) in the inhomogeneous sound velocity field. The ToFs from a sound source to multiple receivers are used to constitute a real time vector. And the ToF from the assumed sound source to each receiver is considered a virtual time vector. It can be calculated according to the distribution of the sound velocity field, regardless of where the sound source is in the field. Then, modified vector and iterative formula can be constructed given the minimum difference between the roles of real and virtual time vectors. By using successive approximation method, the assumed sound source position approaches the actual position systematically until satisfies the localization accuracy requirements. Numerical results indicate that the SALM can considerably improve the localization accuracy. For example, in the 100 m × 100 m horizontal region, the ranging error of the SALM can be lower than 1 mm with more than 20 iterations when the velocity of sound is distributed unevenly within ±5 m/s. Moreover, increasing the number of receivers ensures a significant improvement in the approximate speed. And the localization accuracy can also be improved infinitely by increasing the number of iterations. Finally, the SALM is also applicable to sound source localization in a 3D space.

Analytical Passive Time Reversal Method Combined With Equivalent Source Method for Sound Source Localization in an Enclosure

The analytical passive time reversal method (APTRM) is a powerful technique for sound source localization. In that technique, it generally requires that the frequency response function relating the measurement point to the focusing point should be known in advance. However, inside an enclosure of arbitrary shape, there is no theoretical formulation of this frequency response function, and using the APTRM with the free-field Green's function might lead to inaccurate localization of sound sources. This paper proposes a method combining the APTRM with the equivalent source method (ESM) to locate sound sources in an enclosure of arbitrary shape. In this method, the frequency response function relating the measurement point to the focusing point inside the enclosure is first calculated numerically using the ESM, and then the APTRM with this numerical frequency response function is used to realize the localization of sound sources. Numerical simulations in a rectangular enclosure and an enclosure of arbitrary shape as well as an experiment in a rectangular wooden cabinet are performed to verify the validity of the proposed method. The results demonstrate that the frequency response function in an enclosure can be accurately calculated using the ESM; based on measurements with a spherical array composed of 48 microphones, the proposed method can effectively locate the sound sources in enclosures of different shapes and work stably under the situation of low signal-to-noise ratio.

A modified cross-correlation algorithm to achieve the time difference of arrival in sound source localization

The accuracy in ranging and direction in locating a target source is crucial in sound source localization and different methods have been proposed due to important applications of sound source localization. One of the methods in sound source localization is triangulation with the time difference of arrival information. In this literature, a modified cross-correlation algorithm is introduced to increase the accuracy in time difference of arrival, thus further improving the sound source localization results. A numerical model is generated by assuming multiple sound sources broadcasting in room environment and the location of the target sound source is identified with the triangulation algorithm. Real-time data are produced through experimental setup using an array of four microphones with a target source and background noise. The signals are processed by modified cross-correlation and conventional cross-correlation for comparison. The impacts of the signal-to-noise ratio and time difference of arrival on sound source localization results are demonstrated and discussed. Experimental validation conducted in a non-ideal environment has shown that the modified cross-correlation algorithm can minimize the error in time difference of arrival to be used in sound source localization, thus improving the accuracy in both sound source ranging and direction.

FPGA based dual microphone speech enhancement

This paper proposes an efficient reconfigurable hardware design of dual microphone speech enhancement technique using sound source localization and multi band spectral subtraction methods with elimination of background noise. Firstly, we have used a time delay of arrival algorithm using phase transform (PHAT) to achieve the time difference between the microphone signals. PHAT based filter can reach high SNR gains, which makes it very suitable for localizing the sound source in a microphone array system. After adjustment of the delay between the signals, multi band spectral subtraction technique enhances the signal from the background noise environment in each of the frequency bands. Our design has been implemented in Spartan6 Lx45 FPGA and we have presented the implementation results in terms of subjective and objective evaluations. We have also compared the angular separation between the microphones to the resultant angle between the microphones and observed that proposed design provides very sharp accuracy in every corresponding angle. The objective and subjective evaluation established that our design provides better throughput compared to the existing state of the art research works. The evaluation infers that our proposed hardware induce feasibility for hand-held devices in background noisy environment.

Lightweight and optimized sound source localization and tracking methods for open and closed microphone array configurations

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. Multi-microphone 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.

Measurement Capability of the Interferometric Method of Sound Source Localization in the Absence of Data on the Waveguide Transfer Function

The resolution of interferometric method of a sound source localization is analyzed. Two realizations of this method are presented, which do not call for the knowledge of the propagation medium characteristics for determining the source coordinates. These realizations solve the problem of identifying a low-noise source in water areas where acoustic calibration cannot be performed. The maximum applicability range of the method is estimated; this estimate makes it possible to optimize the source signal processing.

A Computationally efficient IVA-based Blind Source Separation for Hearing Aid Applications and its Real-time Implementation on Smartphone.

Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many real-time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone in real-time to separate speech and noise in realistic scenarios for Hearing-Aid (HA) applications. The experimental results using objective and subjective tests reveal the usefulness of the developed method for real-world applications.