Lee Jun Chang, Nam Kyoung Won, Jang Dong Pyo, Kim In Young
Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
Artif Organs. 2015 Dec;39(12):1061-8. doi: 10.1111/aor.12488. Epub 2015 May 8.
Previously suggested diagonal-steering algorithms for binaural hearing support devices have commonly assumed that the direction of the speech signal is known in advance, which is not always the case in many real circumstances. In this study, a new diagonal-steering-based binaural speech localization (BSL) algorithm is proposed, and the performances of the BSL algorithm and the binaural beamforming algorithm, which integrates the BSL and diagonal-steering algorithms, were evaluated using actual speech-in-noise signals in several simulated listening scenarios. Testing sounds were recorded in a KEMAR mannequin setup and two objective indices, improvements in signal-to-noise ratio (SNRi ) and segmental SNR (segSNRi ), were utilized for performance evaluation. Experimental results demonstrated that the accuracy of the BSL was in the 90-100% range when input SNR was -10 to +5 dB range. The average differences between the γ-adjusted and γ-fixed diagonal-steering algorithms (for -15 to +5 dB input SNR) in the talking in the restaurant scenario were 0.203-0.937 dB for SNRi and 0.052-0.437 dB for segSNRi , and in the listening while car driving scenario, the differences were 0.387-0.835 dB for SNRi and 0.259-1.175 dB for segSNRi . In addition, the average difference between the BSL-turned-on and the BSL-turned-off cases for the binaural beamforming algorithm in the listening while car driving scenario was 1.631-4.246 dB for SNRi and 0.574-2.784 dB for segSNRi . In all testing conditions, the γ-adjusted diagonal-steering and BSL algorithm improved the values of the indices more than the conventional algorithms. The binaural beamforming algorithm, which integrates the proposed BSL and diagonal-steering algorithm, is expected to improve the performance of the binaural hearing support devices in noisy situations.
先前针对双耳听力支持设备提出的对角转向算法通常假定语音信号的方向是预先已知的，但在许多实际情况下并非总是如此。在本研究中，提出了一种基于新的对角转向的双耳语音定位（BSL）算法，并在几种模拟聆听场景中使用实际的噪声中的语音信号评估了BSL算法以及集成了BSL和对角转向算法的双耳波束形成算法的性能。测试声音是在KEMAR人体模型设置中录制的，并且使用了两个客观指标，即信噪比改善（SNRi）和分段信噪比（segSNRi）来进行性能评估。实验结果表明，当输入信噪比在-10至+ 5dB范围内时，BSL的准确率在90%-100%范围内。在餐厅交谈场景中，γ调整和γ固定对角转向算法之间（对于-15至+ 5dB输入信噪比）的平均差异，SNRi为0.203-0.937dB，segSNRi为0.052-0.437dB；在汽车驾驶时聆听场景中，SNRi的差异为0.387-0.835dB，segSNRi的差异为0.259-1.175dB。此外，在汽车驾驶时聆听场景中，双耳波束形成算法的BSL开启和BSL关闭情况之间的平均差异，SNRi为1.631-4.246dB，segSNRi为0.574-2.784dB。在所有测试条件下，γ调整对角转向和BSL算法比传统算法更能提高指标值。集成了所提出的BSL和对角转向算法的双耳波束形成算法有望在嘈杂情况下提高双耳听力支持设备的性能。
