SVD-Based Optimal Filtering for Noise Reduction in Dual Microphone Hearing Aids: A Real Time Implementation and Perceptual Evaluation

Abstract

In this paper, the first real-time implementation and perceptual evaluation of a singular value decomposition (SVD)-based optimal filtering technique for noise reduction in a dual microphone behind-the-ear (BTE) hearing aid is presented. This evaluation was carried out for a speech weighted noise and multitalker babble, for single and multiple jammer sound source scenarios. Two basic microphone configurations in the hearing aid were used. The SVD-based optimal filtering technique was compared against an adaptive beamformer, which is known to give significant improvements in speech intelligibility in noisy environment. The optimal filtering technique works without assumptions about a speaker position, unlike the two-stage adaptive beamformer. However this strategy needs a robust voice activity detector (VAD). A method to improve the performance of the VAD was presented and evaluated physically. By connecting the VAD to the output of the noise reduction algorithms, a good discrimination between the speech-and-noise periods and the noise-only periods of the signals was obtained. The perceptual experiments demonstrated that the SVD-based optimal filtering technique could perform as well as the adaptive beamformer in a single noise source scenario, i.e., the ideal scenario for the latter technique, and could outperform the adaptive beamformer in multiple noise source scenarios.

Design of Linear-Phase Filter Banks with Multiplier-Less Lattice Structures

“…As the coefficient multiplications can be implemented with limited number of additions, the implementation complexity of the filter banks is considerably reduced. Moreover, filter banks are implemented with the lattice structures and the factorization in (14) and (17) and the perfect-reconstruction (PR) property is preserved, despite of the SOPOT approximation. …”

Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock

“…(5), what we need to do is to select some component signals and simply add them together, and then the final denoised result can be obtained. Compared with the traditional procedure, in this process besides that the component signals can be obtained, the final result is also obtained only by the simple addition of the selected component signals; obviously this process is much more convenient and its physical significance is clearer than that of the traditional process.…”

“…where U and V are the orthogonal matrix, UAR m  m , VAR n  n , D is the diagonal matrix, D=[diag (s 1 , s 2 , y, s q ), O] or its transposition, and this is decided by m on or m Zn, DAR m  n , O is the zero matrix, q=min(m, n), s 1 Zs 2 ZyZ s q 40. s i (i=1, 2, y, q) are called the singular values of matrix A. SVD method has been widely applied to many fields in recent years, such as data compression [1,2], system recognition [3], adaptive filter [4,5], principal component analysis (PCA) [6,7], noise reduction [8][9][10], faint signal extraction [11,12], machine condition monitoring [13] and so on. For example, Ahmed et al utilize SVD to compress the electrocardiogram (ECG) signal, their main idea is to transform the ECG signals to a rectangular matrix, compute its SVD, then discard the signals represented by the small singular values and only those signals represented by some big singular values are reserved so that ECG signal can be greatly compressed [1].…”

An Iterative Algorithm for Linear-Phase Paraunitary FIR Filter Banks with Impulse Responses of Different Lengths

“…In [2], biorthogonal filter banks were designed for image compressing by using genetic algorithm. A computationally efficient digital FIR filter bank with adjustable subband distribution is proposed in [3][4] [5]. In [6], an eigenfilter-based method was proposed for the design of 4-band perfect reconstruction (PR) filter banks with linear phase and orthogonality.…”