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Detection of the pitch frequency as the basic characteristic of the voice is one of the most important research subjects in voice analysis and synthesis. In this paper we study, in comparison with the cepstrum method and the modified correlation method, how the BPFP method using bandpass filter pairs and the BPFP-NN method combining the BPFP banks and the neural network (NN) proposed by the authors perform in the presence of pitch disturbance, waveform variation, and added noise when applied to voiced/unvoiced (U/V) detection and pitch extraction. According to the experimental results, the BPFP method and the BPFP-NN method exhibit more stable performance and more effective pitch extraction with U/V detection than the typical cepstrum method and modified correlation method, and the effect is particularly evident at low frequencies due to the logarithmic expression of the center frequency spacing of the BPFP banks. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(5): 24–35, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10045 [ABSTRACT FROM AUTHOR]

Instead of the conventional static memory model based on an associative memory neural network, this paper proposes a dynamic memory model that is extended to the input-output system in order to allow interaction with external information. Based on the time correlation between the input signal and the network output, we analyze and investigate the network response to stored, nonstored, and mixed stored pattern signals, and the parameter dependencies inherent in the model. The possibility of a new mechanism, the chaotic dynamic model, which discriminates and identifies pattern signal information by combining the dynamic memory model with its extension is shown to be possible. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(5): 54–64, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10022 [ABSTRACT FROM AUTHOR]

When a random noise is superposed on a non-Gaussian signal with an abruptly changing component, as in the ease of the image signal, it is impossible for the conventional linear filter to eliminate the noise effectively. This is one of the cases where the nonlinear filter should be applied. This paper proposes a nonlinear digital filter based on the layered neural network, including the optimization of the nonlinear function. The proposed filter can effectively eliminate the noise by utilizing the nonlinearity of the layered neural network and its learning function. Another point is that the performance can be improved by the piecewise-linear approximation of the nonlinear function and the automatic design of the optimal nonlinear function. The effectiveness of the proposed filter is demonstrated by eliminating the noise from the two-dimensional image signal. [ABSTRACT FROM AUTHOR]

Linear adaptive digital filters are applied to various fields for their simplicity in the design and implementation. Considering many kinds of nonlinearities inherent to practical systems, however, nonlinear adaptive filtering will be more desirable. In this paper, we present a design method for parallel nonlinear adaptive digital filters using multilayer nonrecursive neural networks. Linear adaptive digital filters with the advantage of obtaining better accuracy of estimation. Furthermore, the nonlinear adaptive digital filter is extended to parallel nonlinear adaptive digital filters which reduce the computations of each filtering system, especially when a high-order nonlinear adaptive digital filter is necessary. [ABSTRACT FROM AUTHOR]