A FAST AND ACCURATE SOUND SOURCE LOCALIZATION METHOD USING OPTIMAL COMBINATION OF SRP AND TDOA METHODOLOGIES

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.

Accurate localization of sound sources is indispensable for high‐quality sound capture. The difference of distance from a sound source to two microphones causes a time delay of arrival (TDOA). The cross‐power spectrum phase analysis (CSP) method has been proposed to estimate the TDOA based on correlation in signals. The sound source can be localized theoretically as a crossing point of sound directions estimated using two pairs of microphones. However, in a real environment, microphones receive sounds that have originated through reverberation and reflection. This situation makes TDOA estimation difficult, and accurate sound localization is difficult when using only two pairs of microphones. The authors propose a new method of sound localization based on multiple microphones that are widely distributed and placed under the ceiling of a room. A number of pairs of microphones give a series of CSP correlation coefficients as a function of the time delay. The CSP coefficients for hypothetical sound sources are accumulated over all the microphone pairs. The number of microphones is 16. The sound source is localized as a hypothesis that maximizes the accumulated CSP coefficient. Experiment results obtained in a real room show that the proposed method improves the localization accuracy.

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.

Beamforming and its applications in steered-response power (SRP) technology, such as steered-response power delay and sum (SRP-DAS) and steered-response power phase transform (SRP-PHAT), are widely used in sound source localization. However, their resolution and accuracy still need improvement. A novel beamforming method combining SRP and multi-channel cross-correlation coefficient (MCCC), SRP-MCCC, is proposed in this paper to improve the accuracy of direction of arrival (DOA). Directional weight (DW) is obtained by calculating the MCCC. Based on DW, suppressed the non-incoming wave direction and gained the incoming wave direction to improve the beamforming capabilities. Then, sound source localizations based on the dual linear array under different conditions were simulated. Compared with SRP-PHAT, SRP-MCCC has the advantages of high positioning accuracy, strong spatial directivity and robustness under the different signal–noise ratios (SNRs). When the SNR is −10 dB, the average positioning error of the single-frequency sound source at different coordinates decreases by 5.69%, and that of the mixed frequency sound sources at the same coordinate decreases by 5.77%. Finally, the experimental verification was carried out. The results show that the average error of SRP-MCCC has been reduced by 8.14% and the positioning accuracy has been significantly improved, which is consistent with the simulation results. This research provides a new idea for further engineering applications of sound source localization based on beamforming.

The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A SRP-PHAT localization algorithm based on the GSG method is also introduced. The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid. The GSG method thus allows one to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows one to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array. Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure.

In the classic positioning applications, non-line of sight (NLOS) is a fatal problem which has a seriousimpact on the accuracy of target location. In this paper, we use NLOS range measurements based on themultistatic radar system to locate a human targethidingbehind corners. There is a transmitter radar and 5 receiver radars in the multistatic radar system introduced by this paper. When the transmitted antenna hides in the corner, the anti-radiation missiles are difficult in attacking the transmittedantenna. However, the multistatic radar system can locate the target. This paper put forwarded an experiment to illustrate our idea. After obtaining the range measurements of the target, the time difference of arrival (TDOA) method is employed to get estimation of the staff position. The common path can be eliminated by the TDOA method while the scattering points on thewall are unknown. The priori information about scene needs not to be known.

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 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

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.

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.

The higher computational efficiency of the time difference of arrival (TDOA) based sound source localization makes it a preferred choice over steered response power (SRP) methods in real-time applications. However, unlike SRP, its implementation for multiple source localization (MSL) is not straight forward. It includes challenges as accurate feature extraction in unfavourable acoustic conditions, association ambiguity involved in mapping the feature extractions to the corresponding sources and complexity involved in solving the hyperbolic delay equation to estimate the source coordinates. Moreover, the dominating source and early reverberation make the detection of delay associated with the submissive sources further perplexing. Hence, this paper proposes a proficient three-step method for localizing multiple sources from delay estimates. In step 1, the search space region is partitioned into cubic subvolumes, and the delay bound associated with each one is computed. Hereafter, these subvolumes are grouped differently, such that whose associated TDOA bounds are enclosed by a specific delay interval, are clustered together. In step 2, initially, the delay segments and later each subvolume contained by the corresponding delay segment are traced for passing through estimated delay hyperbola. These traced volumes are updated by the weight to measure the likelihood of a source in it. The resultant generates the delay density map in the search space. In the final step, localization enhancement is carried out in the selected volumes using conventional SRP (C-SRP). The validation of the proposed approach is done by carrying out the experiments under different acoustic conditions on the synthesized data and, recordings from SMARD & Audio Visual 16.3 Corpus.

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.

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

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.