Sound source localization using a distributed microphone system in real environments

This paper presents an automatic sound source localization approach based on a combination of the basic time delay estimation sub-methods namely, Time Difference of Arrival (TDOA), and Steered Response Power (SRP) methods. The TDOA method is a fast but vulnerable approach for finding the sound source location in long distances and reverberant environments and is so sensitive in noisy situations. On the other hand, the conventional SRP method is time consuming, but a successful approach to accurately find sound source location in noisy and reverberant environments. Also, another SRP-based method, SRP Phase Transform (SRP-PHAT), has been suggested for the better noise robustness and more accuracy of sound source localization. In this paper, based on the combination of TDOA and SRP based methods, two approaches were proposed for sound source localization. In the first proposed approach called Classical TDOA-SRP, the TDOA method is used to find the approximate sound source direction and then SRP based methods were used to find the accurate location of sound source in the Field of View (FOV) which is obtained by the TDOA method. In the second proposed approach which called Optimal TDOA-SRP, for more reduction of computational processing time of SRP-based methods and better noise robustness, a new criterion has been proposed for finding the effective FOV which is obtained through the TDOA method. Experiments were carried out under different conditions confirming the validity of the purposed approaches.

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.

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.

The use of audio systems that employ binaural synthesis with head tracking has become increasingly popular, particularly in virtual reality gaming systems. The binaural synthesis process uses the Head-Related Transfer Functions (HRTF) as an input required to assign the directions of arrival to sounds coming from virtual sound sources in the created virtual environments. Generic HRTFs are often used for this purpose to accommodate all potential listeners. The hypothesis of the research is that the use of individual HRTF in binaural synthesis instead of generic HRTF leads to improved accuracy and quality of virtual sound source localization, thus enhancing the user experience. A novel methodology is proposed that involves the use of dynamic virtual sound sources. In the experiments, the test participants were asked to determine the direction of a dynamic virtual sound source in both the horizontal and vertical planes using both generic and individual HRTFs. The gathered data are statistically analyzed, and the accuracy of localization is assessed with respect to the type of HRTF used. The individual HRTFs of the test participants are measured using a novel and efficient method that is accessible to a broad range of users.

In this paper, we address the multiple sound source localization problem using time differences of arrival (TDOAs) of sound sources to a microphone array. Typically, TDOAs are estimated based on the peak extraction of the generalized crosscorrelation function. In multi-source cases, for any given microphone pair, it is hard to tell the correspondence between the sound sources and the extracted peaks. In this work, we develop a novel localization approach based on data association which combines multiple TDOAs from the same source across different microphone pairs. Firstly, the generalized cross correlation-phase transform (GCC-PHAT) function is evaluated and multiple peaks of the GCC function indicating candidate TDOAs are extracted for each pair of microphones. Next, we employ the multi-dimensional assignment algorithm to associate multiple TDOAs from the same source. Finally, multiple sound source localization is carried out based on the obtained TDOA associations across different microphone pairs. Experimental results show the proposed method achieves superior performance for multiple sound source localization compared to the competing algorithm, especially in noisy environments.

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.

It remains challenging for robots to accurately perform sound source localization and speech recognition in a real environment with reverberation, noise, and the voices of multiple speakers. Accordingly, we propose “U-TasNet-Beam,” a speech extraction method for extracting only the target speaker’s voice from all ambient sounds in a real environment. U-TasNet-Beam is a neural beamformer comprising three elements: a neural network for removing reverberation and noise, a second neural network for separating the voices of multiple speakers, and a minimum variance distortionless response (MVDR) beamformer. Experiments with simulated data and recorded data show that the proposed U-TasNet-Beam can improve the accuracy of sound source localization and speech recognition in robots compared to the conventional methods in a noisy, reverberant, and multi-speaker environment. In addition, we propose the spatial correlation matrix loss (SCM loss) as a loss function for the neural network learning the spatial information of the sound. By using the SCM loss, we can improve the speech extraction performance of the neural beamformer.

In order to improve the accuracy of sound source localization based on depth learning and reduce the amount of model calculation. In this paper, a principal component analysis sound event localization and detection network (PCA-SELDnet) based on convolution recurrent network model (SELDnet) is proposed, which introduces time domain feature weighted phase transition generalized cross-correlation (GCC-PHAT) and uses principal component analysis (PCA) carries out dimensionality reduction and reconstruction, obtains the features of several dimensions with large contribution, and then combines them with the amplitude spectrum of the original spectrogram for input. The combined features are mapped into two output matrices through SELDnet model, one output is sound event detection matrix and the other output is sound source localization matrix. The results show that PCA-SELDnet improves the accuracy of sound event localization.

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

Users of a cochlear implant (CI) in one ear, who are provided with a hearing aid (HA) in the contralateral ear, so-called bimodal listeners, are typically affected by a constant and relatively large interaural time delay offset due to differences in signal processing and differences in stimulation. For HA stimulation, the cochlear travelling wave delay is added to the processing delay, while for CI stimulation, the auditory nerve fibers are stimulated directly. In case of MED-EL CI systems in combination with different HA types, the CI stimulation precedes the acoustic HA stimulation by 3 to 10 ms. A self-designed, battery-powered, portable, and programmable delay line was applied to the CI to reduce the device delay mismatch in nine bimodal listeners. We used an A-B-B-A test design and determined if sound source localization improves when the device delay mismatch is reduced by delaying the CI stimulation by the HA processing delay (τHA). Results revealed that every subject in our group of nine bimodal listeners benefited from the approach. The root-mean-square error of sound localization improved significantly from 52.6° to 37.9°. The signed bias also improved significantly from 25.2° to 10.5°, with positive values indicating a bias toward the CI. Furthermore, two other delay values (τHA –1 ms and τHA +1 ms) were applied, and with the latter value, the signed bias was further reduced in some test subjects. We conclude that sound source localization accuracy in bimodal listeners improves instantaneously and sustainably when the device delay mismatch is reduced.

This paper presents an active direction-pass filter (ADPF) that separates sounds originating from the specified direction by using a pair of microphones. Its application to front-end processing for speech recognition is also reported. Since the performance of sound source separation by the ADPF depends on the accuracy of sound source localization (direction), various localization modules including the interaural phase difference, interaural intensity difference for each sub-band, and other visual and auditory processing are integrated hierarchically. The resulting performance of auditory localization varies according to the relative position of the sound source. The resolution of the center of the robot is much higher than that of peripherals, indicating similar property of visual fovea. To make the best use of this property, the ADPF controls the direction of a head by motor movement. In order to recognize sound streams separated by the ADPF, a hidden Markov model based automatic speech recognition is built with multiple acoustic models trained by the output of the ADPF under different conditions. A preliminary dialog system is thus implemented on an upper-torso humanoid. The experimental results prove that it works well even when two speakers speak simultaneously.

The aim of this article was to study sound source localization by cochlear implant (CI) listeners with low-frequency (LF) acoustic hearing in both the operated ear and in the contralateral ear. Eight CI listeners had symmetrical LF acoustic hearing and 4 had asymmetrical LF acoustic hearing. The effects of two variables were assessed: (i) the symmetry of the LF thresholds in the two ears and (ii) the presence/absence of bilateral acoustic amplification. Stimuli consisted of low-pass, high-pass, and wideband noise bursts presented in the frontal horizontal plane. Localization accuracy was 23° of error for the symmetrical listeners and 76° of error for the asymmetrical listeners. The presence of a unilateral CI used in conjunction with bilateral LF acoustic hearing does not impair sound source localization accuracy, but amplification for acoustic hearing can be detrimental to sound source localization accuracy.

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, 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

Accuracy of unknown sound source localization and lifetime of sensor nodes is one of the important and critical issue and challenge in wireless sensor network. Localization plays major role when there is an uncertainty of the exact source location of some fixed or mobile devices. The main problem is source localization in an environment where they try to localize and focus on a source of unknown sound and noisy distance. This paper presents a new approach on the indoor localization of an acoustic sensor source. In this localization system, the environment will be scattered with a swarm of acoustic motes. The motes used in this localization system are the LOTUS motes from MEMSIC. In order to get the system working, We utilize a modular sensor board to plug on the mote. This sensor board captures acoustic signals and then digitalizes them. This localization algorithm uses the incoming Time Difference of Arrival (TDOA) values of a swarm ZigBee modules. The location of the source of the acoustic signal is determined by the multilateration technique. A sensor network is used in order to collect the signals. The evident advantage of the algorithm is that it requires synchronization between the motes mutually and not between the motes and acoustic source. The algorithm for these methods is written in MATLAB. The motes communicate in a ZigBee mesh network.