Smoothed Frame-Level SINR and Its Estimation for Sensor Selection in Distributed Acoustic Sensor Networks

Shanzheng Guan, Mou Wang, Zhongxin Bai, Jianyu Wang, Jingdong Chen, Jacob Benesty

科研成果: 期刊稿件文章同行评审

摘要

Distributed acoustic sensor network (DASN) refers to a sound acquisition system that consists of a collection of microphones randomly distributed across a wide acoustic area. Theory and methods for DASN are gaining increasing attention as the associated technologies can be used in a broad range of applications to solve challenging problems. However, unlike traditional microphone arrays or centralized systems, properly exploiting the redundancy among different channels in DASN is facing many challenges including but not limited to variations in pre-amplification gains, clocks, sensors' response, and signal-to-interference-plus-noise ratios (SINRs). Selecting appropriate sensors relevant to the task at hand is therefore crucial in DASN. In this work, we propose a speaker-dependent smoothed frame-level SINR estimation method for sensor selection in multi-speaker scenarios, specifically addressing source movement within DASN. Additionally, we devise an approach for similarity measurement to generate dynamic speaker embeddings resilient to variations in reference speech levels. Furthermore, we introduce a novel loss function that integrates classification and ordinal regression within a unified framework. Extensive simulations are performed and the results demonstrate the efficacy of the proposed method in accurately estimating smoothed frame-level SINR dynamically, yielding state-of-the-art performance.

源语言英语
页(从-至)4554-4568
页数15
期刊IEEE/ACM Transactions on Audio Speech and Language Processing
32
DOI
出版状态已出版 - 2024

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