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A new scheme is developed, in this paper, within the framework of the ADPCM-based waveform coding technique for low bit rate encoding of speech signals. The essential feature of this scheme consists of replacing the commonly used linear filter with nonlinear processing based on kernel methods. Our previously reported study, conducted on various emerging kernel adaptive algorithms, shows the usefulness of the kernel LMS (KLMS) algorithm in this framework. However, two original strategies are incorporated into this scheme, in the current study, to further improve its performance. The first strategy is based on improving the adaptive scalar quantization of the residual samples by employing a look-ahead concept to find the best possible quantization levels using the Viterbi algorithm. The second strategy is to apply a pre-emphasized noise reduction filter. This filter is implemented in a closed-loop form along with an inverse filter, so as to minimize the destructive effects of the noise reduction filter. Simultaneous employment of these strategies in the main scheme with the nonlinear processing provided by the KLMS algorithm brings about a waveform encoder that reconstructs speech with PESQ measure of 2.5 at low bit rate of 1 bit per sample.

Keywords: Kernel Least Mean Square, Look-Ahead Quantization, Low Bit Rate Speech Coding, Pre-Emphasized Noise Reduction

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