Галерея 2958408
Галерея 2958408
Hierzu gibt es keine Street View-Bilder.
Hierzu gibt es keine Street View-Bilder.
All Books Conferences Courses Journals & Magazines Standards Authors Citations
Abstract: Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming co... View more
Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming coherent auditory objects and achieving a comprehensive spatial scene understanding suggests pursuing a joint solution of the two problems. This article presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system. Both tasks are joined through the use of spatial stream segregation which produces probabilistic time-frequency masks for individual sources attributable to separate locations, enabling segregated sound event detection operating on these streams. We use simulations of a comprehensive suite of test scenes with multiple co-occurring sound sources, and propose performance measures for systematic investigation of the impact of scene complexity on this segregated detection of sound types. Analyzing the effect of spatial scene arrangement, we show how a robot could facilitate high performance through optimal head rotation. Furthermore, we investigate the performance of segregated detection given possible localization error as well as error in the estimation of number of active sources. Our analysis demonstrates that the proposed approach is an effective method to obtain joint sound event location and type information under a wide range of conditions.
Date of Publication: 09 December 2019
References is not available for this document.
IEEE Account
Change Username/Password
Update Address
Purchase Details
Payment Options
Order History
View Purchased Documents
Need Help?
US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support
Realistic aural environments consist of numerous co-occurring different sounds emitted from sources distributed in space. Computational auditory scene analysis thus involves the development of models that draw information from audio streams and assign semantic labels to auditory objects. For instance, a robotic system that is specialized to search and rescue missions should be able to detect the presence of a fire, an alarm that is going off, screaming victims, or a crying baby, and localize them. Two key issues therefore are (a) detecting sound events and their types within that stream, commonly called audio or sound event detection (SED), and (b) localizing the corresponding sources emitting the sounds, denoted sound source localization (SSL). This work investigates the combination of the two: joint sound event localization and detection (SELD).
Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380)
2011 4th International Conference on Intelligent Networks and Intelligent Systems
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
Сексуальная блондинка Кэролин Риз дрочит хуй своему любовнику между большими красивыми сиськами
Неверная блондинка изменяет своему бизнесмену с негром
Подружки у которых нет парней