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

Sound source localization techniques in array signal processing have been interested for many years. A lot of direction-of-arrival (DOA) estimation methods have been proposed. Most of them use plane waves as model. On the other hand, 3D sound source localization techniques are important for many applications. They are required to estimate the DOA and the distance of sound sources. In the case of the techniques targeted to estimate the distances of sound sources, monopoles must be used for the model. The positions of monopoles are candidates for the sound source positions. However, it is difficult to handle a large number of monopoles placed at various distances. Moreover, it is difficult to decide the number of sound sources to be estimated from the candidates. The proposed method uses sparse estimation with a monopole dictionary in frequency domain, as is well known that the effectiveness of sparse representation for DOA estimation has been in late years. After the sparse estimation, the proposed method ...

This paper describes a method for estimating Direction Of Arrival (DOA) of multiple sound sources in full azimuth with three microphones. Estimating DOA with paired microphone arrays creates imaginary sound sources because of time delay of arrival (TDOA) being identical between real and imaginary sources. Imaginary sound sources can create chronic problems in multiple Sound Source Localization (SSL), because they can be localized as real sound sources. Our proposed approach is based on the observation that each microphone array creates imaginary sound sources, but the DOA of imaginary sources may be different depending on the orientation of the paired microphone array. With the fact that a real source would always be localized in the same direction regardless of the array orientation, we can suppress the imaginary sound sources by minimum filtering based on Steered Response Power — Phase Transform (SRP-PHAT) method. A set of experiments conducted in a real noisy environment showed that the proposed method was accurate in localizing multiple sound sources.

In this paper, brief explanation will be given about system that can localize sound source based on FPGA, this system use five microphones with predefined distance, which will be used to acquire the Time Delay of Arrival (TDOA) data. TDOA is the parameter needed by sound source to arrive at corresponding microphone. The focus in this paper is providing high accuracy 3D sound source localization with efficient area and energy. In this paper, the solutions are achieved by designing 3D sound source localization with efficient cross correlation process, by using only one adder and one multiplier, this paper also use CORDIC algorithm to create more efficient system. The design is then implemented using Altera Cyclone II achieving only 18237 logic elements and 8194 registers.

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

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

In this paper, we present an approach to sound localization for moving sound sources. We use four conventional microphones and a multi-channel sound recording device for capturing sound signals. A database with different types of moving sound source scenarios is created and one of the scenarios is analysed in this paper. This scenario involves walking of a person and creating sounds simultaneously at certain positions in a room, and our aim is to determine the location of the person as three dimensions. We use time difference of arrival for 3D sound source localization. The time delays between sounds channels are obtained by excitation source information based time-delay estimation algorithm. In addition, signal processing methods are used for increasing the success of moving sound source localization. Savitzky-Golay and threshold filters are utilized to pre-filter the recorded sounds and removing the background noise signals, respectively. The non-linear equation of the sound localization is solved by using the modified Levenberg–Marquardt Algorithm (LMA) on the Time Difference of Arrival (TDOA) process. The results for the 3D localization of the moving sound source are obtained by measuring the differences between the real and estimated positions of the person.DOI: http://dx.doi.org/10.5755/j01.eie.23.4.18724

For a hands-free speech interface, it is very important to capture distant talking speech with high quality. A microphone array is an ideal candidate for this purpose. However, this approach requires localizing the target talker. Conventional talker localization algorithms in multiple sound source environments not only have difficulty localizing the multiple sound sources accurately, but also have difficulty localizing the target talker among known multiple sound source positions. To cope with these problems, we propose a new talker localization algorithm consisting of two algorithms. One is DOA (Direction Of Arrival) estimation algorithm for multiple sound source localization based on CSP (Cross-power Spectrum Phase) coefficient addition method. The other is statistical sound source identification algorithm based on GMM (Gaussian Mixture Model) for localizing the target talker position among localized multiple sound sources. In this paper, we particularly focus on the talker localization performance based on the combination of these two algorithms with a microphone array.

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.

This article presents common methods for sound source localization and separation, and their applications to robots.Sound source localization is an estimate of the location or direction of a sound source, whereas sound source separation extracts each sound source from a mixture of sound sources. Two approaches are possible for both technologies: binaural and microphone array processing. Sound source localization and separation have been widely studied in the fields of psychoacoustics, signal processing, and robotics. In binaural research, the human auditory capability is explored to solve both localization and separation problems. However, this approach is limited in terms of performance. Thus, microphone array processing is also being investigated as a primary topic in signal processing. Since robots are among the most promising applications of sound source localization and separation, robot audition is introduced and real‐time methods (such as the software platform for robot audition called Honda Research Institute Japan Audition for Robot with Kyoto University (HARK)) to deal with real‐world problems are explained.

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.

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.

It is very important to capture distant-talking speech for a hands-free speech interface with high quality. A microphone array is an ideal candidate for this purpose. However, this approach requires localizing the target talker. Conventional talker localization algorithms in multiple sound source environments not only have difficulty localizing the multiple sound sources accurately, but also have difficulty localizing the target talker among known multiple sound source positions. To cope with these problems, we propose a new talker localization algorithm consisting of two algorithms. One is DOA (direction of arrival) estimation algorithm for multiple sound source localization based on CSP (cross-power spectrum phase) coefficient addition method. The other is statistical sound source identification algorithm based on GMM (Gaussian mixture model) for localizing the target talker position among localized multiple sound sources. In this paper, we particularly focus on the talker localization performance based on the combination of these two algorithms with a microphone array. We conducted evaluation experiments in real noisy reverberant environments. As a result, we confirmed that multiple sound signals can be identified accurately between ‘‘speech’’ or ‘‘non-speech’’ by the proposed algorithm. [Work supported by ATR, and MEXT of Japan.]

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.

We applied motion theory to robot audition to improve the inadequate performance. Motions are critical for overcoming the ambiguity and sparseness of information obtained by two microphones. To realise this, we first designed a sound source localisation system integrated with cross-power spectrum phase (CSP) analysis and an EM algorithm. The CSP of sound signals obtained with only two microphones was used to localise the sound source without having to measure impulse response data. The expectation-maximisation (EM) algorithm helped the system to cope with several moving sound sources and reduce localisation errors. We then proposed a way of constructing a database for moving sounds to evaluate binaural sound source localisation. We evaluated our sound localisation method using artificial moving sounds and confirmed that it could effectively localise moving sounds slower than 1.125 rad/s. Consequently, we solved the problem of distinguishing whether sounds were coming from the front or rear by rotating and/or tipping the robot's head that was equipped with only two microphones. Our system was applied to a humanoid robot called SIG2, and we confirmed its ability to localise sounds over the entire azimuth range as the success rates for sound localisation in the front and rear areas were 97.6% and 75.6% respectively.

Multiple sound source localization is one of the important topic in speech processing. GCC function is used as a traditional algorithm for sound source localization. This function estimates DOA for multiple speakers by calculation the cross-correlation between microphone signals but its accuracy decreases in adverse conditions. The aim of proposed method in this paper is localization of multiple simultaneous speakers in undesirable condition. The proposed method is based on novel 3D nested microphone array in combination with obtained information of Discrete Wavelet Transform (DWT) and subband processing. The proposed 3D nested microphone array prepares the condition for 3D localization and eliminates the spatial aliasing between microphone signals. Also, we propose the DWT for extraction the information of speech signal. Since, the spectral information of speech signal concentrates on low frequencies, we propose a structure of filter bank based on DWT to increase the frequency resolution on low frequencies. The performed evaluation on real and simulated data shows the superiority of our proposed method in comparison with Fullband and subband processing with uniform filters and uniform microphone array.