Suppr超能文献

一种真实、会话的信噪比估计方法。

A method for realistic, conversational signal-to-noise ratio estimation.

机构信息

Hearing Systems Section, Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.

Widex Aktieselskab, Lynge, Denmark.

出版信息

J Acoust Soc Am. 2021 Mar;149(3):1559. doi: 10.1121/10.0003626.

Abstract

The analysis of real-world conversational signal-to-noise ratios (SNRs) can provide insight into people's communicative strategies and difficulties and guide the development of hearing devices. However, measuring SNRs accurately is challenging in everyday recording conditions in which only a mixture of sound sources can be captured. This study introduces a method for accurate in situ SNR estimation where the speech signal of a target talker in natural conversation is captured by a cheek-mounted microphone, adjusted for free-field conditions and convolved with a measured impulse response to estimate its power at the receiving talker. A microphone near the receiver provides the noise-only component through voice activity detection. The method is applied to in situ recordings of conversations in two real-world sound scenarios. It is shown that the broadband speech level and SNR distributions are estimated more accurately by the proposed method compared to a typical single-channel method, especially in challenging, low-SNR environments. The application of the proposed two-channel method may render more realistic estimates of conversational SNRs and provide valuable input to hearing instrument processing strategies whose operating points are determined by accurate SNR estimates.

摘要

对真实环境会话信噪比(SNR)的分析可以深入了解人们的交际策略和困难,并指导听力设备的开发。然而,在日常录音条件下,仅能捕捉到声源的混合,准确测量 SNR 具有挑战性。本研究介绍了一种准确的现场 SNR 估计方法,其中通过贴脸颊的麦克风捕获目标说话者的语音信号,将其调整为自由场条件,并与测量的脉冲响应卷积,以估计其在接收说话者处的功率。接收器附近的麦克风通过语音活动检测提供仅噪声分量。该方法应用于两个真实声音场景中的现场录音。结果表明,与典型的单通道方法相比,所提出的方法更能准确估计宽带语音水平和 SNR 分布,尤其是在挑战性的低 SNR 环境中。所提出的双通道方法的应用可能会对会话 SNR 进行更真实的估计,并为听力仪器处理策略提供有价值的输入,其工作点由准确的 SNR 估计确定。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验