Sound localization problems are usually tackled by the acquisition of data from phased microphone arrays and the application of acoustic holography or beamforming algorithms. However, the number of sensors required to achieve reliable results is often prohibitive, particularly if the frequency range of interest is wide. It is shown that the number of sensors required can be reduced dramatically providing the sound field is time stationary. The use of scanning techniques such as “Scan & Paint” allows for the gathering of data across a sound field in a fast and efficient way, using a single sensor and webcam only. It is also possible to characterize the relative phase field by including an additional static microphone during the acquisition process. This paper presents the theoretical and experimental basis of the proposed method to localise sound sources using only one fixed microphone and one moving acoustic sensor. The accuracy and resolution of the method have been proven to be comparable to large microphone arrays, thus constituting the so called “virtual phased arrays”.

Passive source localization in shallow water has always been an important and challenging problem. Implementing scientific research, surveying, and monitoring using a short, less than ten meter long, horizontal linear array has received considerable attention in the recent years. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, it is usually difficult to obtain a sufficient spatial gain for localizing long-range sources due to its limited physical size. To address this problem, a localization approach is proposed which is based on matched-field processing of the likelihood of the passive source localization in shallow water, as well as inter-position processing for the improved localization performance and the enhanced stability of the estimation process. The ability of the proposed approach is examined through the two-dimensional synthetic test cases which involves ocean environmental mismatch and position errors of the short array. The presented results illustrate the localization performance for various source locations at different signal- to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the short array moves at a low speed along a straight line at a certain depth.

The paper presents results of the localization of main noise sources in the industrial plant. Identification of main noise sources was made with an acoustic camera using Beamforming Method. Parallel to the measurements by means of the acoustic camera, sound level measurements on the main noise sources have been performed. Based on the calculations, prediction regarding the noise emission at residential buildings located near to the plant has been determined. Acoustic noise maps have been performed with LEQ Professional software, which includes the 3D geometry of the buildings inside the plant. It has been established that, after introduction of noise reduction measures in the plant, the noise levels at the observation points in the residential area meets the limit values.

Simultaneous perception of audio and visual stimuli often causes concealment or misrepresentation of information actually contained in these stimuli. Such effects are called the "image proximity effect" or the "ventriloquism effect" in the literature. Until recently, most research carried out to understand their nature was based on subjective assessments. The authors of this paper propose a methodology based on both subjective and objectively retrieved data. In this methodology, objective data reflect the screen areas that attract most attention. The data were collected and processed by an eye-gaze tracking system. To support the proposed methodology, two series of experiments were conducted - one with a commercial eye-gaze tracking system Tobii T60, and another with the Cyber-Eye system developed at the Multimedia Systems Department of the Gdańsk University of Technology. In most cases, the visual-auditory stimuli were presented using a 3D video. It was found that the eye-gaze tracking system did objectivize the results of experiments. Moreover, the tests revealed a strong correlation between the localization of a visual stimulus on which a participant's gaze focused and the value of the "image proximity effect". It was also proved that gaze tracking may be useful in experiments which aim at evaluation of the proximity effect when presented visual stimuli are stereoscopic.

In order to solve the problem of large error of delay estimation in low SNR environment, a new delay estimation method based on cross power spectral frequency domain weighting and spectrum subtraction is proposed. Through theoretical analysis and MATLAB simulation, among the four common weighting functions, it is proved that the cross-power spectral phase weighting method has a good sharpening effect on the peak value of the cross-correlation function, and it is verified that the improved spectral subtraction method generally has a good noise reduction effect under different SNR environments. Finally, the joint simulation results of the whole algorithm show that the combination of spectrum subtraction and crosspower spectrum phase method can effectively sharpen the peak value of cross-correlation function and improve the accuracy of time delay estimation in the low SNR environment. The results of this paper can provide useful help for sound source localization in complex environments.

The microphone data collected in aeroacoustic wind tunnel test contains not only desired aeroacoustic signal but also background noise generated by the jet or the valve of the wind tunnel, so the desired aeroacoustic characteristics is difficult to be highlighted due to the low Signal-to-Noise Ratio (SNR). Classical cross spectral matrix removal can only reduce the microphone self-noise, but its effect is limited for jet noise. Therefore, an Airflow Background Noise Suppression method based on the Ensemble Empirical Mode Decomposition (ABNSEEMD) is proposed to eliminate the influence of background noise on aeroacoustic field reconstruction. The new method uses EEMD to adaptively separate the background noise in microphone data, which has good practicability for increasing SNR of aeroacoustic signal. A localization experiment was conducted by using two loudspeakers in wind tunnel with 80 m/s velocity. Results show that proposed method can filter out the background noise more effectively and improve the SNR of the loudspeakers signal compared with spectral subtraction and cepstrum methods. Moreover, the aeroacoustic field produced by a NACA EPPLER 862 STRUT airfoil model was also measured and reconstructed. Delay-and-sum beamforming maps of aeroacoustic source were displayed after the background noise was suppressed, which further demonstrates the proposed method’s advantage.

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

The acoustic vector sensor (AVS) is used to measure the acoustic intensity, which gives the direction-ofarrival (DOA) of an acoustic source. However, while estimating the DOA from the measured acoustic intensity the finite microphone separation (d) in a practical AVS causes angular bias. Also, in the presence of noise there exists a trade off between the bias (strictly increasing function of d) and variance (strictly decreasing function of d) of the DOA estimate. In this paper, we propose a novel method for mitigating the angular bias caused due to finite microphone separation in an AVS. We have reduced the variance by increasing the microphone separation and then removed the bias with the proposed bias model. Our approach employs the finite element method (FEM) and curves fitting to model the angular bias in terms of microphone separations and frequency of a narrowband signal. Further, the bias correction algorithm based on the intensity spectrum has been proposed to improve the DOA estimation accuracy of a broadband signal. Simulation results demonstrate that the proposed bias correction scheme significantly reduces the angular bias and improves the root mean square angular error (RMSAE) in the presence of noise. Experiments have been performed in an acoustic full anechoic room to corroborate the effect of microphone separation on DOA estimation and the efficacy of the bias correction method.