Abstract
In this letter, we propose a novel residual echo suppression (RES) algorithm based on a statistical model constructed in the acoustic echo cancellation framework. In the proposed approach, all the possible near-end and far-end signal conditions are classified into four distinct hypotheses, and the power spectral density estimation is carried out according to the result of hypothesis testing. The distribution of each signal component is characterized by a parametric model, and the conventional likelihood ratio test is performed to make an optimal decision. The experimental results show that the proposed algorithm yields improved performance compared to that of the previous RES technique.
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.
