Abstract
In environments in which multiple simultaneously-active acoustic sources contribute to sensor responses, Blind Source Separation (BSS) signal processing techniques may be employed to separate (that is, estimate or reconstruct) the signal characteristics of hidden sources. Only Mostly Blind Source Separation (OMBSS) involves the estimation of similar sources in important contexts in which non-acoustic information is also available about one or more of the contributing sources. Recently-reported objective source separation performance measures confirm that non-acoustic information can be used effectively to support high-quality separation in situations in which traditional BSS methods perform poorly (e.g., when more sources are active than there are microphones available). Here we present the results of additional perceptual and objective tests showing that OMBSS processing enhances the intelligibility of speech recorded in the presence of multiple simultaneous speech-babble and non-speech maskers.
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.
