Improvement of Range Estimation with Microphone Array

Efficient and robust sound source recognition and localization is one of the basic techniques for humanoid robots in terms of reaction to environments. Due to the fixed sensor arrays and limited computation resources in humanoid robots, there comes challenge for sound source recognition and localization. This article proposes a sound source recognition and localization framework to realize real-time and precise sound source recognition and localization system using humanoid robots’ sensor arrays. The type of the audio is recognized according to the cross-correlation function. And steered response power-phase transform function in discrete angle space is used to search the sound source direction. The sound source recognition and localization framework presents a new multi-robots collaboration system to get the precise three-dimensional sound source position and introduces a distance weighting revision way to optimize the localization performance. Additionally, the experiment results carried out on humanoid robot NAO demonstrate that the proposed approaches can recognize and localize the sound source efficiently and robustly.

Beamforming technology is an essential method in acoustic imaging or reconstruction, which has been widely used in sound source localization and noise reduction. The beamforming algorithm can be described as all microphones in a plane simultaneously recording the source signal. The source position is then localized by maximizing the result of the beamformer. Evidence has shown that the accuracy of the sound source localization in a 2D plane can be improved by the non-synchronous measurements of moving the microphone array. In this paper, non-synchronous measurements are applied to 3D beamforming, in which the measurement array envelops the 3D sound source space to improve the resolution of the 3D space. The entire radiated object is covered better by a virtualized large or high-density microphone array, and the range of beamforming frequency is also expanded. The 3D imaging results are achieved in different ways: the conventional beamforming with a planar array, the non-synchronous measurements with orthogonal moving arrays, and the non-synchronous measurements with non-orthogonal moving arrays. The imaging results of the non-synchronous measurements are compared with the synchronous measurements and analyzed in detail. The number of microphones required for measurement is reduced compared with the synchronous measurement. The non-synchronous measurements with non-orthogonal moving arrays also have a good resolution in 3D source localization. The proposed approach is validated with a simulation and experiment.

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.

Optimization of number of microphones and microphone spacing using time delay based multilateration approach for explosive sound source localization

Sound Source Localization (SSL) is an essential function for robot audition and yields the location and number of sound sources, which are utilized for post-processes such as sound source separation. SSL for a robot in a real environment mainly requires noise-robustness, high resolution and real-time processing. A technique using microphone array processing, that is, Multiple Signal Classification based on Standard EigenValue Decomposition (SEVD-MUSIC) is commonly used for localization. We improved its robustness against noise with high power by incorporating Generalized EigenValue Decomposition (GEVD). However, GEVD-based MUSIC (GEVD-MUSIC) has mainly two issues: 1) the resolution of pre-measured Transfer Functions (TFs) determines the resolution of SSL, 2) its computational cost is expensive for real-time processing. For the first issue, we propose a TF interpolation method integrating time-domain-based and frequency-domain-based interpolation. The interpolation achieves super-resolution SSL, whose resolution is higher than that of the pre-measured TFs. For the second issue, we propose two methods, MUSIC based on Generalized Singular Value Decomposition (GSVD-MUSIC), and Hierarchical SSL (H-SSL). GSVD-MUSIC drastically reduces the computational cost while maintaining noise-robustness in localization. H-SSL also reduces the computational cost by introducing a hierarchical search algorithm instead of using greedy search in localization. These techniques are integrated into an SSL system using a robot embedded microphone array. The experimental result showed: the proposed interpolation achieved approximately 1 degree resolution although we have only TFs at 30 degree intervals, GSVD-MUSIC attained 46.4% and 40.6% of the computational cost compared to SEVD-MUSIC and GEVD-MUSIC, respectively, H-SSL reduces 59.2% computational cost in localization of a single sound source.

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.

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.

Acoustical inverse problems regularization: Direct definition of filter factors using Signal-to-Noise Ratio

Sound source localization based on a microphone array can be used in telephone or video conference systems to detect the speaker's location. A method based on time difference of arrival (TDOA) estimation is an ideal candidate as an effective method for sound source localization. Time delay estimation (TDE) and source localization are two main factors of it. The crosspower-spectrum phase (CSP) method is a popular TDE technique in sound source localization based on microphone arrays. The relatively precise TDE can be achieved by using the CSP method in the presence of weak noise and reverberation. The performance degrades dramatically in the case of low signal-to-noise ratio (SNR) and strong reverberation. A generalized CSP (GCSP) method for TDE is proposed to improve the performance of CSP. The weighting function of GCSP is estimated from the multi-channel signals of the microphone array. Simulation results show that the proposed method outperforms the CSP method.

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.

An intelligent algorithm was developed based on backpropagation artificial neural network for the acoustic emission source localization. The established and trained methods of the algorithm were stated with the time difference of arrival detected by a fiber optic acoustic emission sensor array and the coordinates of acoustic emission source. The response characteristic of fiber optic acoustic emission sensor was calibrated with the commercial piezoelectric ceramic transducer (PZT) acoustic emission sensor, which provided that the fiber optic acoustic emission sensor was better suited to detect the low frequency of stress wave than the PZT sensor. Four fiber optic acoustic emission sensors were deployed in a square array in an aluminum plate for comparisons between different algorithms of source localization. Comparison results of acoustic emission source location provided that the intelligent algorithm improved the accuracy by reducing the nonlinear errors. For the anisotropic materials, a sensor array deployed in a diamond pattern was adopted. The velocities of stress waves in orthogonal directions were measured as the basic performance for both algorithms of source localization. Four sensors were integrated into a carbon fiber–reinforced polymer plate as a perfect structure for locating the acoustic emission source impacting on its surface. The experiment results provide that the maximum error is only 6.3 mm using the intelligent algorithm.

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.

The paper presents a method of analysis of electric motors based on acoustic measurements. Acoustic signals were measured by microphones arranged in circular shape array called acoustic camera. Measurements by acoustic camera allow localization of sound source and by that separate sounds of interests from background noise can be obtained. Typically acoustic camera measurements are accompanied by a photography associated with acoustic measurement scene which is used for mapping of sound sources on the monitored area. As a reference, acoustic analysis is compared with vibration measurements. Vibration signals were measured by piezoelectric accelerometers and data collector dedicated for condition monitoring of electric motors. As presented in the result section, acoustic analysis appears as a valuable technique for condition monitoring of electric motors even in noisy industrial environment.

Chapter 15 - A novel sound source localization method using a global-best guided cuckoo search algorithm for drone-based search and rescue operations

Three dimensional passive acoustic spatial identification of nucleations in superheated droplet detectors