Phase calibration method for microphone array based on multiple sound sources

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.

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.]

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.

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.

The estimation of distance and power of multiple sound sources by combining three-dimensional sound intensity and beamforming

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.

Real-time and robust sound source tracking is an important function for a robot operating in a daily environment, because the robot should recognize where a sound event such as speech, music and other environmental sounds originate from. This paper addresses real-time sound source tracking by real-time integration of an in-room microphone array (IRMA) and a robot-embedded microphone array (REMA). The IRMA system consists of 64 ch microphones attached to the walls. It localizes multiple sound sources based on weighted delay-and-sum beam-forming on a 2D plane. The REMA system localizes multiple sound sources in azimuth using eight microphones attached to a robot's head on a rotational table. The localization results are integrated to track multiple sound sources by using a particle filter in real-time. The experimental results show that particle filter based integration improved accuracy and robustness in multiple sound source tracking even when the robot's head was in rotation

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.

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.

Synchronistical localization, separation, and reconstruction for multiple sound sources are usually necessary in various situations, such as in conference rooms, living rooms, and supermarkets. To improve the intelligibility of speech signals, the application of deep neural networks (DNNs) has achieved considerable success in the area of time-domain signal separation and reconstruction. In this paper, we propose a hybrid microphone array signal processing approach for the nearfield scenario that combines the beamforming technique and DNN. Using this method, the challenge of identifying both the sound source location and content can be overcome. Moreover, the use of a sequenced virtual sound field reconstruction process enables the proposed approach to be quite suitable for a sound field which contains a dominant, stronger sound source and masked, weaker sound sources. Using this strategy, all traceable, mainly sound, sources can be discovered by loops in a given sound field. The operational duration and accuracy of localization are further improved by substituting the broadband weighted multiple signal classification (BW-MUSIC) method for the conventional delay-and-sum (DAS) beamforming algorithm. The effectiveness of the proposed method for localizing and reconstructing speech signals was validated by simulations and experiments with promising results. The localization results were accurate, while the similarity and correlation between the reconstructed and original signals was high.

This paper assesses the 3D sound IP phone service (3D IP phone) developed by the authors under environment with multiple sound sources by experiment. The assessment consists of evaluation of the sound source localization, subjective evaluation and objective one. The authors utilize two sound sources that are conversations carried on by a man and a woman. From the experimental results, this paper shows that multiple sound sources can promote listener's reproduction of sound images. This means that the proposed system can give the higher feeling of being in more real environment.

How to improve the performance of multiple sound source localization method, this paper studies the parameter and process design in multiple sound source position. First, it divides a signal into several sub-frames, and it calculates signal variation; then it uses the clustering algorithm to divide the estimated values into several categories; finally it calculates the final sound source location estimation. Experimental results show that the positioning algorithm is effective. There are many parameters will affect the gunfire correct rate and error rate, the parameters and angle selection will affect sound source position estimation.

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.

Sound source localization is the problem of estimating the positions of one or several sound sources. In terms of binaural audio, localization is a paramount perceptual characteristic which can be assessed subjectively or objectively. For objective evaluation of binaural sound localization, typical methods exploit binaural or monaural cues to predict directions of sound sources. Since multiple sound sources are often perceived simultaneously in daily sound scenes, an objective sound localization model which can detect temporally overlapping sources is required. In this paper, we propose a binaural multiple sound source localization network (BMSSLnet) model, which can predict framewise azimuths without prior knowledge of sound source number in a binaural audio signal. We implement multiple azimuth prediction as a multi-label classification task, and propose to use separated multi-label cross-entropy and mean square error as the loss function. Experimental results show that the proposed model obtains the average precision of 0.9 and 0.75 for spatial prediction on the anechoic dataset and reverberant dataset with up to three temporally overlapping sources, respectively. Framewise temporal prediction with average accuracy of 38.3 ms is achieved.

Mobile robots communicating with people would benefit from being able to detect sound sources to help localize interesting events in real-life settings. We propose using a spherical robot with four microphones to determine the spatial locations of multiple sound sources in ordinary rooms. The arrival temporal disparities from phase difference histograms are used to calculate the time differences. A precedence effect model suppresses the influence of echoes in reverberant environments. To integrate spatial cues of different microphones, we map the correlation between different microphone pairs on a 3D map corresponding to the azimuth and elevation of sound source direction. Results of experiments indicate that our proposed system provides sound source distribution very clearly and precisely, even concurrently in reverberant environments with the Echo Avoidance (EA) model.