کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6959515 1451960 2015 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks
ترجمه فارسی عنوان
روشهای پرتوهای کمینه-واریانس توزیع بهینه برای تقویت گفتار در شبکه های حسگر بی سیم بی سیم
کلمات کلیدی
شبکه های سنسورهای صوتی بی سیم، تقویت گفتار توزیع، حداقل پرتو فریم واریانس،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
In multiple speaker scenarios, the linearly constrained minimum variance (LCMV) beamformer is a popular microphone array-based speech enhancement technique, as it allows minimizing the noise power while maintaining a set of desired responses towards different speakers. Here, we address the algorithmic challenges arising when applying the LCMV beamformer in wireless acoustic sensor networks (WASNs), which are a next-generation technology for audio acquisition and processing. We review three optimal distributed LCMV-based algorithms, which compute a network-wide LCMV beamformer output at each node without centralizing the microphone signals. Optimality here refers to equivalence to a centralized realization where a single processor has access to all signals. We derive and motivate the algorithms in an accessible top-down framework that reveals their underlying relations. We explain how their differences result from their different design criterion (node-specific versus common constraints sets), and their different priorities for communication bandwidth, computational power, and adaptivity. Furthermore, although originally proposed for a fully connected WASN, we also explain how to extend the reviewed algorithms to the case of a partially connected WASN, which is assumed to be pruned to a tree topology. Finally, we discuss the advantages and disadvantages of the various algorithms
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 107, February 2015, Pages 4-20
نویسندگان
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