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”.
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.