一种改善言语清晰度的子带自适应降噪算法
Sub-band adaptive noise reduction algorithm to improve speech intelligibility

助听器对声音进行压缩放大，需要高言语清晰度的降噪算法。该文提出了一种子带自适应噪声抑制方法，通过加权重叠相加滤波器组和基于心理声学模型的子带划分、基于先验和后验信噪比的快变的非线性降噪增益、基于噪声声压级估值的慢变的增益下限阈值、基于峰值跟踪的子带增益平滑及其跟踪和释放时间系数的精细选择等算法，明显提高了言语清晰度。主观测听实验表明：该方法对输入的不同信噪比的带噪语音的言语清晰度提高约12%~45%。在EZAIRO5900数字信号处理器上实现了此方法，通过对增益公式的量化处理使得整个算法的运行效率提高约30%。
Abstract：Noise reduction algorithms to improve speech intelligibility are needed when sounds are compressed and amplified in hearing aids. A sub-band adaptive noise reduction algorithm was developed with a weighted overlap-add filter bank and psycho-acoustic model for the sub-band splitting. The non-linear noise reduction gains are computed with an estimated a posteriori signal to noise ratio (SNR) and an a priori SNR. The gain floors are determined based on the estimated noise level expressed as the dB sound pressure level (SPL). The final gains are smoothed between the frames by a peak detector with carefully selected attack and release time constants. Listening tests show 12% to 45% improvements in intelligibility by this algorithm for noise corrupted speech. A quantified gain table is also used to replace the non-linear gain computing when the algorithm is implemented on the EZAIRO5900 digital signal processor, with the execution cycle reduced by about 30%.