Level-Crossing Sampling with Multiple Temporal Resolutions for Speech Signals

Abstract

Level-crossing sampling (LCS) is an alternative to the uniform sampling method and is suitable for sparse and bursty signals. LCS, which is adaptive to amplitude variations of the signals, mainly focuses on reducing the total number of samples. Presently, speech signals are sampled with LCS methods that have a constant temporal resolution, and they require time-to-digital converters (TDC) with a wide dynamic range. A long sequence of overflow counts of the TDC counter may be generated during silence regions if the dynamic range is reduced. This paper proposes level-crossing samplers with multiple temporal resolutions for speech signals. Frequency scaling is applied to standard LCS, adaptive LCS and peak sampling. During the silence region, the frequency of the TDC clock is scaled down, which enables the representation of longer intervals with smaller words. The number of bits per sample and hence the total number of samples generated are decreased due to the reduction in the dynamic range of the TDC. Simulation results indicate that the proposed method outperforms the conventional level-crossing samplers, which do not employ frequency scaling. In addition to a significant reduction in data size, this method can also be used for real-time automatic detection of silence regions in a speech signal.