Optimal Non-Uniform Sampling by Branch-and-Bound Approach for Speech Coding

Speech coding plays a significant role in voice communication and improving network bandwidth efficiency for applications that require long-distance communication or storage space utilization. Non-uniform sampling (NUS) is a technique for the same, which performs data reduction by sampling at irregular intervals. In the literature, researchers use the structural property of the speech waveform for studying various NUS methods, such as LCSS, MMD, IPD, and zero-crossing point. However, in this paper, we consider the speech signal’s statistical properties to propose an optimal NUS approach. The proposed technique statistically analyzes the speech signal to sample the abrupt changes over a time frame and approximates the signal with minimal reconstruction error using cost and linear penalty functions for avoiding the over-fitting problem. The proposed technique further performs the optimization using the branch-and-bound. To evaluate the proposed NUS, we design a speech waveform encoder called Block Adaptive Amplitude Sampling (BAAS). A BAAS encoder can directly perform statistical analysis on the speech waveform to select data samples corresponding to the most significant changes in the signal. The decoder approximates the eliminated values using linear interpolation. We experimentally study the proposed technique using various matrices and measures such as POLQA and MUSHRA test. The evaluation shows that the proposed NUS technique retains only 25% of data samples to get an acceptable quality signal regeneration. In addition, comparative studies with MMD and IPD show that the proposed algorithm performs 1.6% better with 30% lower MSE scores.