Efficient Multiband Algorithms for Blind Source Separation

The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture, we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide range of signal to noise ratios

Electroacoustic and Behavioural Evaluation of Hearing Aid Digital Signal Processing Features

Modern digital hearing aids provide an array of features to improve the user listening experience. As the features become more advanced and interdependent, it becomes increasingly necessary to develop accurate and cost-effective methods to evaluate their performance. Subjective experiments are an accurate method to determine hearing aid performance but they come with a high monetary and time cost. Four studies that develop and evaluate electroacoustic hearing aid feature evaluation techniques are presented. The first study applies a recent speech quality metric to two bilateral wireless hearing aids with various features enabled in a variety of environmental conditions. The study shows that accurate speech quality predictions are made with a reduced version of the original metric, and that a portion of the original metric does not perform well when applied to a novel subjective speech quality rating database. The second study presents a reference free (non-intrusive) electroacoustic speech quality metric developed specifically for hearing aid applications and compares its performance to a recent intrusive metric. The non-intrusive metric offers the advantage of eliminating the need for a shaped reference signal and can be used in real time applications but requires a sacrifice in prediction accuracy. The third study investigates the digital noise reduction performance of seven recent hearing aid models. An electroacoustic measurement system is presented that allows the noise and speech signals to be separated from hearing aid recordings. It is shown how this can be used to investigate digital noise reduction performance through the application of speech quality and speech intelligibility measures. It is also shown how the system can be used to quantify digital noise reduction attack times. The fourth study presents a turntable-based system to investigate hearing aid directionality performance. Two methods to extract the signal of interest are described. Polar plots are presented for a number of hearing aid models from recordings generated in both the free-field and from a head-and-torso simulator. It is expected that the proposed electroacoustic techniques will assist Audiologists and hearing researchers in choosing, benchmarking, and fine-tuning hearing aid features