A learning algorithm of neural networks for spectrum envelope estimation
Hiroyuki Kamata, N. Matsumoto, Yoshihisa Ishida · 2002
We estimate the spectrum envelope of speech by using neural networks. The neural networks system proposed in our study consists of two parts. The first part is the network for detecting the spectrum peaks, and the other is for interpolating between the extracted peaks. The structure of the latter is derived from the Neville interpolation. The former is based on the peak picking method. We present an operator for detecting the peaks using neural networks. Unfortunately, the result is that the neural networks do not only detect the general peaks but also the local peaks and the extremely closed peaks. We train the neural networks to detect only the general peaks. The learning algorithm uses a steepest descent method such as the backpropagation (BP) algorithm. After the learning, we show the detected peaks and the spectrum envelope when we use the learned pattern as the input data. In addition, we perform an experiment to examine whether the proposed operator corresponds to the XOR Boolean function or not.