Achieving 3D Beamforming by Non-Synchronous Microphone Array Measurements.

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.

The non-synchronous measurement (NSM) technology has been significantly developed. NSM at the coprime position (CP-NSM) is a measurement principle in two-dimensional (2D) acoustic imaging, wherein a planar array is moved to a coprime position for measurements. However, there are certain drawbacks to the measurement principles of spherical arrays in three-dimensional (3D) acoustic imaging. A measurement principle of 3D acoustic imaging has been investigated using 3D Non-Synchronous Measurements with a Central Reference based on Revolution and Autorotation (CR-NSM). The primary contributions of this CR-NSM are as follows: (1) A measurement principle for 3D non-synchronous measurements with a central reference based on revolution and autorotation is proposed. (2) The spatial resolution is primarily determined by the revolution in CR-NSM, and the side lobe is reduced by autorotation in CR-NSM. In the simulation results, the spatial resolution is obtained using CR-NSM for good imaging at low signal-to-noise ratios (SNR). Moreover, the cross-spectral matrix (CSM) completion error is enhanced by adding the phase relations between consecutive positions. The CR-NSM algorithm was developed according to the measurement principles of 3D acoustic imaging.

To develop effective means for suppressing the noise of the jet, it is necessary to have information on the distribution of acoustic sources in turbulent flows. This problem can be solved by measuring the noise with a microphone array, the data from which are processed by certain mathematical algorithms, which ultimately gives information on the location and sound pressure levels of noise sources at frequencies of interest. A number of well-known producers offer ready-made commercial solutions, the disadvantage of which is their high cost. In addition, such microphone arrays are usually tuned to a certain dynamic range in a given frequencies and do not allow modification if necessary. In this connection, the aim of this work is to develop own microphone array, which allows optimization of the microphones arrangement in the required range of sound source localization and evaluation its quality in measuring the noise of a turbulent jet.

In the microphone array, the phase error of each microphone causes a deviation in sound source localization. At present, there is a lack of effective methods for phase error calibration of the entire microphone array. In order to solve this problem, a phase mismatch calculation method based on multiple sound sources is proposed. This method requires collecting data from multiple sound sources in turn, and constructing a nonlinear equation setthrough the signal delay and the geometric relationship between the microphones and the sound source positions. The phase mismatch of each microphone can be solved from the nonlinear equation set. Taking the single frequency signal as an example, the feasibility of the method is verified by experiments in a semi-anechoic chamber. The phase mismatches are compared with the calibration results of exchanging microphone. The difference of the phase error values measured by the two methods is small. The experiment also shows that the accuracy of sound source localization by beamforming is improved. The method is efficient for phase error calibration of arrays with a large number of microphones.

There are many applications that would be aided by the determination of the physical position and orientation of users. Some of the applications include service robots, video conference, intelligent living environments, security systems and speech separation for hands-free communication devices (Coen, 1998; Wax & Kailath, 1983; Mungamuru & Aarabi, 2004; Sasaki et al., 2006; Lv & Zhang 2008). As an example, without the information on the spatial location of users in a given environment, it would not be possible for a service robot to react naturally to the needs of the user. To localize a user, sound source localization techniques are widely used (Nakadai et al., 2000; Brandstein & Ward, 2001; Cheng & Wakefield, 2001; Sasaki et al., 2006). Sound localization is the process of determining the spatial location of a sound source based on multiple observations of the received sound signals. Current sound localization techniques are generally based upon the idea of computing the time difference of arrival (TDOA) information with microphone arrays (Knnapp & Cater, 1976; Brandstein & Silverman, 1997). An efficient method to obtain TDOA information between two signals is to compute the cross-correlation of the two signals. The computed correlation values give the point at which the two signals from separate microphones are at their maximum correlation. When only two isotropic (i.e., not directional as in the mammalian ear) microphones are used, the system experiences front-back confusion effect: the system has difficulty in determining whether the sound is originating from in front of or behind the system. A simple and efficient method to overcome this problem is to incorporate more microphones (Huang et al., 1999). Various weighting functions or pre-filters such as Roth, SCOT, PHAT, Eckart filter and HT can be used to increase the performance of time difference estimation (Knnapp & Cater, 1976). However, the performance improvement is achieved with the penalty of large power consumption and hardware overhead, which may not be suitable for the implementation of portable systems such as service robots. In this chapter, we propose an efficient sound source localization method under the assumption that three isotropic microphones are used to avoid the front-back confusion

Aiming at the leakage problem of ship pipeline system, this paper proposes a leak detection method of ship pipeline system, which is based on video target detection and tracking algorithm and sound source localization algorithm of microphone array, and uses the image information collected by camera and audio information collected by microphone array for multimodal information fusion to improve the robustness of sound source localization system and the accuracy of leak detection results. In this paper, the whole audio-video processing method is introduced theoretically, the video detection and tracking module adopts YOLOv5m algorithm as the framework of visual detection, the source localization module adopts cross-type microphone array to obtain audio information, and the time delay of each microphone received signal is combined to calculate the sound source orientation, and finally the system software and hardware design are carried out, compared with the single-modal algorithm and system, this system has higher tracking accuracy and performance compared to a single modal algorithm and system.

3D sound source localization based on coherence-adjusted monopole dictionary and modified convex clustering

Accurate localization of multiple sound sources is indispensable for the microphone array-based high quality sound capture. For single sound source localization, the CSP (cross-power spectrum phase analysis) method has been proposed. The CSP method localizes a sound source as a crossing point of sound directions estimated using different microphone pairs. However, when localizing multiple sound sources, the CSP method has a problem that the localization accuracy is degraded due to cross-correlation among different sound sources. To solve this problem, this paper proposes a new method which suppresses the undesired cross-correlation by synchronous addition of CSP coefficients derived from multiple microphone pairs. Experiment results in a real room showed that the proposed method improves the localization accuracy when increasing the number of the synchronous addition.

With the development of artificial intelligence voice technology, array signal processing technology has been widely used in intelligent human–computer interaction. In this field, the sound source localization technology of the microphone array system is also one of the key technologies in the composition of intelligent systems, and it is also a research hotspot technology, which is of great significance for improving human–computer interaction ability. In this paper, the microphone array technology is used to demonstrate and improve the sound source localization of indoor speech signals. A spatial distributed microphone array localization algorithm is proposed to improve the accuracy of the indoor voice source signal localization in the presence of indoor noise and reverberation.

This paper presentsanew approach for the three-dimensional (3-D) localization of sound sources. An acoustic camera uses an angular beamforming to measure the Direction of Arrival (Do A) of an incoming signal, to localize the emission source. The acoustic sensor used in this article is the Brüel & Kjaer acoustic camera transformed to operate inabistatic mode. The transformation consists inaplacing of one of the microphones of the acoustic camera outside of its microphone array. This allows simultaneous estimation of the Do Aand the Time Difference of Arrival (TDo A) of the incoming signal(s). Such sensors were not found. The paper proposes emitter localization in range - cross range - elevation coordinates by combining estimates of TDo Aand Do Aand presents the signal processing method for that purpose. The range resolution of 0.2mwas achieved in an experiment. Experimental results were obtained using different emission sources. Adescription of resolution cell limitations is presented. The obtained results show acoustic noise source localization without the pre-metering of the range of the imaging plane, i.e., withoutaneed to use the additional range meter which is notapart of the acoustic camera. The latter is important in tasks of non-destructive testing.

In this position paper, a fast real-time algorithm for the sound source localization system using microphone array is proposed. The algorithm includes three steps: Delay time estimation using time difference of arrival (TDOA); Searching area selection based on angle of arrival; and, Sound source localization using a newly golden section searching (GSS) method. Thanks to the first two steps, we can narrow down the searching area significantly. Then, the real-time sound source localization is achieved by the effective GSS method. Finally, satisfactory simulation and experimental results are illustrated.

There is no consensus regarding the benefit of implantable hearing aids in congenital unilateral conductive hearing loss (UCHL). This study aimed to measure sound source localization performance in patients with congenital UCHL and contralateral normal hearing who received a new bone conduction implant. Evaluation of within-subject performance differences for sound source localization in a horizontal plane. Tertiary referral center. Five patients with atresia of the external auditory canal and contralateral normal hearing implanted with transcutaneous bone conduction implant at the Medical University of Vienna were tested. Activated/deactivated implant. Sound source localization test; localization performance quantified using the root mean square (RMS) error. Sound source localization ability was highly variable among individual subjects, with RMS errors ranging from 21 to 40 degrees. Horizontal plane localization performance in aided conditions showed statistically significant improvement compared with the unaided conditions, with RMS errors ranging from 17 to 27 degrees. The mean RMS error decreased by a factor of 0.71 (p < 0.001). Analysis revealed improved sound localization performance in a horizontal plane with the activated transcutaneous bone conduction implant. Some patients with congenital UCHL might be capable of developing improved horizontal plane localization abilities with the binaural cues provided by this device.

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.

LaSNet: An end-to-end network based on steering vector filter for sound source localization and separation

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.