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OverviewAcoustic source localization is an essential component in many modern day audio applications. For example, smart speakers require localization capabilities in order to determine the speakers in the scene and their role. Based on the location information, they can enhance a speaker or carry out location specific tasks, such as switching the lights on and off, steering a camera, etc. Localization has often been based on creating physical models which become extremely intricate in real-world applications. Recently, researchers have started using learning techniques to address localization problems.This monograph introduces the reader to the research and practical aspects behind the approach of learning the characteristics of the acoustic environment directly from the data rather than using a predefined physical model. Written by the experts in the field who have developed many of these techniques, it provides a comprehensive overview and insights into this burgeoning area of acoustic developments. The reader is introduced to the underlying mathematics before being introduced to the localization problem in depth. The core paradigm of using manifolds for diffusion mapping and distance is then described. Building on these concepts, the authors address both single and multiple manifold localization. Finally, manifold-based tracking is covered. Data-Driven Multi-Microphone Speaker Localization on Manifolds is an illuminating introduction to designing and building acoustic systems where localization of multi-microphone and speakers forms an essential part of the system. Full Product DetailsAuthor: Bracha Laufer-Goldshtein , Ronen Talmon , Sharon GannotPublisher: now publishers Inc Imprint: now publishers Inc Weight: 0.257kg ISBN: 9781680837360ISBN 10: 1680837362 Pages: 176 Publication Date: 06 October 2020 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. Background 2. Mathematical Foundations 3. Data Model and Acoustic Features 4. From High-Dimensional Representation to Low-Dimensional Manifold 5. Data-Driven Source Localization: A Single Microphone Pair 6. Bayesian Perspective 7. Data-Driven Source Localization: Ad Hoc Array 8. Data-Driven Speaker Tracking 9. Summary and Future Directions ReferencesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |