Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios.

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

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.

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.

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.

While various methods for sound source localization have been developed, most of them utilize on the time difference of arrival (TDOA) between microphones or the measured head related transfer functions (HRTF). In case of a real robot implementation, the former has a merit of light computation load to estimate the sound direction but can not consider the effect of platform on TDOAs, while the latter can, because characteristics of robot platform are included in HRTF. However, the latter needs large resources for the HRTF database of a specific robot platform. We propose the compensation method which has the light computation load while the effect of platform on TDOA can be taken into account. The proposed method is used with spherical head related transfer function (SHRTF) on the assumption that robot platform, for example a robot head, installed microphones can be modeled to a sphere. We verify that the proposed method decreases the estimation error caused by the robot platform through the simulation.

This paper describes a DSP implementation of probabilistic sound source localization algorithm. Time Delay of Arrival (TDOA) between two microphones through cross-correlation method has been used for sound localization in our robot platforms. However, since cross-correlation values are highly dependent on the upcoming sound signal and acoustic environment, the time delay values and the corresponding localization results are easily perturbed. Here, a probabilistic method for sound source localization is presented. And its DSP implementation is applied to the active audition system that we developed.

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.

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.

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.

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.

Confidence intervals of location of calling marine mammals, derived from time differences of arrival (TDOA) between receivers, depend on errors of TDOAs, receiver location, clocks, sound speeds, and location method. Simulations demonstrate Ishmael, a TDOA locator based on uncorrected least squares minimization (ULSM), yields errors with mean, standard deviation, and maximum of 0.1, 0.2, and 0.9 km, respectively, due to sensitivity to inputs and numerical implementation when applied to scenarios with minuscule errors; e.g., five clock-synchronized receivers residing on the vertices of a square with one in its center. This sensitivity can mask other causes of location error due to small uncertainties in receiver location and sound speed. Realistic uncertainties of sound speed up to ±7.5 m/s lead to errors up to 4 km. With unsynchronized clocks and common practice of correcting TDOA from synchronization measurements at the start and end of an experiment, errors of location are 10 to 1000 km. These problems occur because ULSM was not designed to account for all errors. ULSM is also available in PAMGuard and other systems and is used to study behavior and abundance of calling marine mammals. ULSM is briefly compared to another method designed to account for errors.

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.

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.

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

The localization of a speaker inside a closed environment is often approached by real-time processing of multiple audio signals captured by a set of microphones. One of the leading related methods for sound source localization, the steered-response power (SRP), searches for the point of maximum power over a spatial grid. High-accuracy localization calls for a dense grid and/or many microphones, which tends to impractically increase computational requirements. This paper proposes a new method for sound source localization (called H-SRP), which applies the SRP approach to space regions instead of grid points. This arrangement makes room for the use of a hierarchical search inspired by the branch-and-bound paradigm, which is guaranteed to find the global maximum in anechoic environments and shown experimentally to also work under reverberant conditions. Besides benefiting from the improved robustness of volume-wise search over point-wise search as to reverberation effects, the H-SRP attains high performance with manageable complexity. In particular, an experiment using a 16-microphone array in a typical presentation room yielded localization errors of the order of 7 cm, and for a given fixed complexity, competing methods' errors are two to three times larger.