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Environment-aware Listener-Optimized Binaural Enhancement of Speech (E-LOBES)

环境感知听者优化双耳语音增强 (E-LOBES)

基本信息

批准号：
EP/M026698/1
负责人：
David Brookes
金额：
$ 125.33万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM026698%2F1
关键词：
Environment aware Listener Optimized Binaural

项目摘要

Age-related hearing loss affects over half the UK population aged over 60. Hearing loss makes communication difficult and so has severe negative consequences for quality of life. The most common treatment for mild-to-moderate hearing loss is the use of hearing aids. However even with aids, hearing impaired listeners are worse at understanding speech in noisy environments because their auditory system is less good at separating wanted speech from unwanted noise. One solution for this is to use speech enhancement algorithms to amplify the desired speech signals selectively while attenuating the unwanted background noise.It is well known that normal hearing listeners can better understand speech in noise when listening with two ears rather than with only one. Differences between the signals at the two ears allow the speech and noise to be separated based on their spatial locations resulting in improved intelligibility. Technological advances now make feasible the use of two hearing aids that are able to share information via a wireless link. By sharing information in this way, it becomes possible for the speech enhancement algorithms within the hearing aids to localize sound sources more accurately and, by jointly processing the signals for both ears, to ensure that the spatial cues that are present in the acoustic signals are retained. It is the goal of this project to exploit these binaural advantages by developing speech enhancement algorithms that jointly enhance the speech received by the two ears.Most current speech enhancement techniques have evolved from the telecommunications industry and are designed to act only on monaural signals. Many of the techniques can improve the perceived quality of already intelligible speech but binary masking is one of the few techniques that has been shown to improve the intelligibility of noisy speech for both normal and hearing impaired listeners. In the binary masking approach regions of the time-frequency domain that contain significant speech energy are left unchanged while regions that contain little speech energy are muted. In this project we will extend existing monaural binary masking techniques to provide binaural speech enhancement while preserving the inter-aural time and level differences that are critical for the spatial separation of sound sources.To train and tune our binaural speech enhancement algorithm we will also develop within the project an intelligibility metric that is able to predict the intelligibility of a speech signal for a binaural listener with normal or impaired hearing in the presence of competing noise sources. This metric is the key to finding automatically the optimum settings an individual listener's hearing aids in a particular environment.The final evaluation and development of the binaural enhancement algorithm assess speech perception in noise in a panel of hearing-impaired listeners who will also be asked to assess the quality of the enhanced speech signals.

与听力损失相关的听力损失影响超过一半的60岁以上的英国人口。听力损失使沟通变得困难，因此对生活质量产生严重的负面影响。助听器的选配是助听器选配的基础。然而，即使有助听器，听力受损的听众在嘈杂的环境中理解语音的能力也很差，因为他们的听觉系统不太善于将想要的语音与不想要的噪音分开。一种解决方案是使用语音增强算法来选择性地放大所需的语音信号，同时衰减不需要的背景噪声。在两个耳朵处的信号之间的差异允许语音和噪声基于它们的空间位置被分离，从而导致提高的可懂度。技术进步现在使得使用两个助听器成为可能，这两个助听器能够通过无线链路共享信息。通过以这种方式共享信息，助听器内的语音增强算法可以更准确地定位声源，并且通过联合处理双耳的信号，确保保留声学信号中存在的空间线索。本项目的目标是通过开发语音增强算法来利用这些双耳优势，该算法联合增强双耳接收到的语音。目前大多数语音增强技术都是从电信行业发展而来的，并且被设计为仅作用于单声道信号。许多技术可以提高已经可理解的语音的感知质量，但二进制掩蔽是已被证明可以提高正常和听力受损的听众的嘈杂语音的可理解性的少数技术之一。在二进制掩蔽方法中，包含显著语音能量的时频域区域保持不变，而包含很少语音能量的区域被静音。在这个项目中，我们将扩展现有的单声道二进制掩蔽技术，以提供双耳语音增强，同时保留间，为了训练和调整我们的双耳语音增强算法，我们还将在项目中开发一个可懂度度量，该度量能够预测双耳收听者的语音信号的可懂度，在竞争性噪声源存在时听力受损。这个指标是自动找到最佳设置的关键个别听众的助听器在一个特定的environment.The双耳增强算法的最终评估和发展评估语音感知噪声中的一个小组的听力受损的听众谁也将被要求评估增强语音信号的质量。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Active speech level estimation in noisy signals with quadrature noise suppression

具有正交噪声抑制的噪声信号中的主动语音电平估计

DOI：
10.1109/eusipco.2016.7760437
发表时间：
2016
期刊：
影响因子：
0
作者：
Dionelis N
通讯作者：
Dionelis N

Localization Experiments with Reporting by Head Orientation: Statistical Framework and Case Study

按头部方向进行报告的本地化实验：统计框架和案例研究

DOI：
10.17743/jaes.2017.0038
发表时间：
2017
期刊：
Journal of the Audio Engineering Society
影响因子：
1.4
作者：
De Sena E
通讯作者：
De Sena E

Robust Source Counting and Acoustic DOA Estimation using Density-Based Clustering

使用基于密度的聚类进行稳健的源计数和声学 DOA 估计

DOI：
10.1109/sam.2018.8448889
发表时间：
2018
期刊：
影响因子：
0
作者：
Hafezi S
通讯作者：
Hafezi S

Personalized HRTFs for hearing aids

助听器的个性化 HRTF

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
A. Moore
通讯作者：
A. Moore

Single-Channel Online Enhancement of Speech Corrupted by Reverberation and Noise

DOI：
10.1109/taslp.2016.2641904
发表时间：
2017-03
期刊：
IEEE/ACM Transactions on Audio, Speech, and Language Processing
影响因子：
0
作者：
Clement S. J. Doire;M. Brookes;P. Naylor;Christopher M. Hicks;Dave Betts;M. Dmour;S. H. Jensen
通讯作者：
Clement S. J. Doire;M. Brookes;P. Naylor;Christopher M. Hicks;Dave Betts;M. Dmour;S. H. Jensen