Noise reduction is a critical aspect of hearing aids that researchers trying to solve over the years. Most of the noise reduction algorithms are evaluated using English Speech Material. There are many differences between the linguistic features of English and Sinhala languages, such as different syllable structures and different vowel duration. Both wavelet transformation and adaptive filtering have been widely used for noise reduction in hearing aids. This paper compares the performance of wavelet transformation of ten wavelet families with soft and hard thresholding methods against adaptive filters with Normalized Least Mean Square (NLMS), Least Mean Square (LMS), Average Normalized Least Mean Square (ANLMS), Recursive Least Square (RLS), and Adaptive Filtering Averaging (AFA) optimization algorithms along with cepstral and energy-based voice activity detection (VAD) algorithms. The performance evaluation is done using objective metrics; Signal to Noise Ratio (SNR) and Perceptual Evaluation of Speech Quality (PESQ) and a subjective metric; Mean Opinion Score (MOS). The NOIZEUS database by the University of Texas, Dallas and a newly formed Sinhala language audio database were used for the evaluation.
翻译:噪声减少是助听器的关键方面,研究人员多年来一直在寻求解决方案。大部分噪声减少算法使用英语语音材料进行评估。英语和斯里兰卡语之间有许多语言特征的差异,例如不同的音节结构和不同的元音时长。小波变换和自适应滤波已被广泛应用于助听器中的噪声减少中。本文比较了十个小波家族的小波变换(软和硬阈值方法)与具有归一化最小均方(NLMS)、最小均方(LMS)、平均归一化最小均方(ANLMS)、递归最小方(RLS)和自适应滤波平均值(AFA)优化算法的自适应滤波,以及基于倒谱和能量的语音活动检测(VAD)算法的性能。使用客观度量标准:信噪比(SNR)和语音质量的感知评估(PESQ)以及主观度量标准:平均意见分数(MOS)进行性能评估。使用德克萨斯大学达拉斯分校的NOIZEUS数据库和新形成的斯里兰卡语音频数据库进行评估。