In some situations of active noise control, IIR filters are more suitable than FIR filters, due to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control (ANC), however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and of relatively slow convergence speed for noise composed of narrow band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed; it uses and updates the lattice form adaptive IIR filter in an active noise control system. The comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.

The performance of widely-used adaptive noise canceling (ANC) deteriorates much when the desired signal is leaked into the reference channel or when there are uncorrelated noise present in the reference channel. This paper proposes a dual-microphone scheme, named iterative noise canceling (INC), to overcome the drawbacks mentioned above. The proposed INC system, in which a codebook-based speech quality measure is employed to control a modified iterative Wiener filter (MIWF), can automatically reduce noises in the primary input until convergence occurs. In comparison with the traditional ANC algorithm, the evaluation using real noise and voices recorded in a car shows the noise reduction performance is dramatically improved, even in cases where the reference SNR is close to 0 dB.

We present a novel approach to vector quantization in which a feature vector is represented by a binary vector. It is called binary quantization (BQ). The performance criterion of vector quantization, distortion (distance) measure, was employed for investigating the effectiveness of BQ. At 12 b/analysis frame, the average distortion caused by BQ is even lower than the intraspeaker average distance between two repetitions of the same word (after DTW alignment). Since the output of BQ is a binary sequence, it is possible to combine it with a forward Hamming net classifier. In terms of the idea of a hierarchical model for describing a speaker individual characteristics, a text-independent speaker identification system was set up. Experimental results show that the performance of this system is very good. Not only are the small memory space and little computation required, in the speaker identification system, but, more importantly, it shows strong robustness in additive Gaussian white noise.

A novel approach to establish membership functions of fuzzy states based on a functional-link neural network (FLNN) for text-independent speaker identification is proposed. Parameters in corresponding states derived from training utterances uttered by each speaker are fed to a fuzzy statistical model to form the histograms of the states. Data in normalized histograms are used as learning samples for the FLNNs. A set of FLNNs is used as a speaker's model. For each unknown speaker's voice in the test, parameters are fed to every FLNN corresponding to each fuzzy state respectively, the FLNN generalizes the degree of membership to the state, then the system scores the degree of membership to the speaker's model and chooses the speaker whose model's degree of membership is the highest.