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Acoustic sound source localization using signal processing is required in order to estimate the direction from where a particular acoustic source signal is coming and it is also important in order to find a solution for hands free communication. Video conferencing, hand free communications are different applications requiring acoustic sound source localization. These applications need a robust algorithm which can reliably localize and position the acoustic sound sources. The Steered Response Power Phase Transform (SRP-PHAT) is an important and robust algorithm to localize acoustic sound…mehr

Produktbeschreibung
Acoustic sound source localization using signal processing is required in order to estimate the direction from where a particular acoustic source signal is coming and it is also important in order to find a solution for hands free communication. Video conferencing, hand free communications are different applications requiring acoustic sound source localization. These applications need a robust algorithm which can reliably localize and position the acoustic sound sources. The Steered Response Power Phase Transform (SRP-PHAT) is an important and robust algorithm to localize acoustic sound sources. However, the algorithm has a high computational complexity thus making the algorithm unsuitable for real time applications. This thesis focuses on describe the implementation of the SRP-PHAT algorithm as a function of source type, reverberation levels and ambient noise. The main objective of this thesis is to present different approaches of the SRP-PHAT to verify the algorithm in terms ofacoustic environment, microphone array configuration, acoustic source position and levels of reverberation and noise.
Autorenporträt
Ajoy Kumar Dey has completed his M.Sc in Electrical Engineering Emphasis on Signal Processing at Blekinge Institute of Technoogy, Karlskrona, Sweden. His research interests are applied mathematical model, such as topological network theory, Signal Processing and Acoustic Speech Processing and mathematical foundations of neural network.