This research presents an audio coding scheme, based on sub-band coding (SBC), with the implementation of quasi-logarithmic compandors. The presented coding scheme is based on signal decomposition and individual processing of the different sub-bands. Two SBC schemes for audio coding are presented, a non-adaptive and an adaptive coding scheme. The application of backward adaptation technique further improves the performance of this coding scheme, especially when using smaller compression factor values. This paper also describes the determination of an efficient bit allocation, used for coding the individual sub-bands. The results indicate that the proposed coding schemes can successfully be implemented in audio signal coding, providing a high quality output signal.
In this paper, the performance of quasi-logarithmic quantizer, designed for correlated discrete input signal is analyzed. Quantizer design is done for Laplacian source due to its both hardware and software significance, whereas experiments are done by processing test wideband speech signal sampled at 16 [kHz]. The quantizer is exploited as a second stage of two-stage quantization system, where the first step is used for continuous signal sampling, while the second stage provides additional data compression. The main goal is to provide improved design by discussing theoretical performance of two quantization models. As the traditional models for performance estimation provide estimation of average performance, we have decided to propose a novel model for performance estimation and to analyze performance in details for each random input signal variance. Finally, the experimental results have shown excellent matching with theoretical results.
The paper proposes a novel speech signal coding scheme that implements a simple transform coding and forward adaptive quantization. The proposed scheme is adapted to the input signal variance, providing highly efficient bandwidth usage, whereas implemented transform coding provides sub-sequences with more predictable signal characteristics, so that more suitable signal processing can be performed. The aforementioned transform coding precedes adaptive quantization, providing additional compression. The objective quality measure used for system performance estimation is SQNR (signal-to-quantization-noise ratio), which represents a standard measure for lossy coding types. The influence of transform coding is discussed by comparing the obtained results with the corresponding one achieved by applying only the same adaptive quantization. Furthermore, the comparison with system performance of PCM (pulse-code modulation) coding system confirms that the proposed coding scheme has a lot of potential for further implementation, since that the proposed system ensures SQNR gain up to 4.0983 [dB] for various values of system parameters.
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