Abstract
The accuracy in ranging and direction in locating a target source is crucial in sound source localization and different methods have been proposed due to important applications of sound source localization. One of the methods in sound source localization is triangulation with the time difference of arrival information. In this literature, a modified cross-correlation algorithm is introduced to increase the accuracy in time difference of arrival, thus further improving the sound source localization results. A numerical model is generated by assuming multiple sound sources broadcasting in room environment and the location of the target sound source is identified with the triangulation algorithm. Real-time data are produced through experimental setup using an array of four microphones with a target source and background noise. The signals are processed by modified cross-correlation and conventional cross-correlation for comparison. The impacts of the signal-to-noise ratio and time difference of arrival on sound source localization results are demonstrated and discussed. Experimental validation conducted in a non-ideal environment has shown that the modified cross-correlation algorithm can minimize the error in time difference of arrival to be used in sound source localization, thus improving the accuracy in both sound source ranging and direction.
Highlights
Sound source localization (SSL) and acoustic mappings are essential for the analysis of noise and vibration reduction processes
time of arrival (TOA) describes how much time the sound travels from the source to a sensor, and the time difference of arrival (TDOA) specifies how much time the sound arrives in one sensor compared to the reference sensor
We have addressed the challenge of making such improvements with a modified crosscorrelation (MCC) algorithm where errors in signals are extensively minimized through peak detections and singularities caused by random noise are eliminated
Summary
Sound source localization (SSL) and acoustic mappings are essential for the analysis of noise and vibration reduction processes. An MCC result is used as TDOA in this algorithm, and experimental setup including four-microphone arrays, background noise, and target source in a typical classroom or laboratorytype environments are used to produce real-time data for processing.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
