The speech coding scheme based on the simple transform coding and forward adaptive quantization for discrete input signal processing is proposed in this paper. The quasi-logarithmic quantizer is applied for discretization of continuous input signal, i.e. for preparing discrete input. The application of forward adaptation based on the input signal variance provides more efficient bandwidth usage, whereas utilization of transform coding provides sub-sequences with more predictable signal characteristics that ensure higher quality of signal reconstruction at the receiving end. In order to provide additional compression, transform coding precedes adaptive quantization. As an objective measure of system performance, signal-to-quantization-noise ratio is used. System performance is discussed for two typical cases. In the first case, it was considered that the information about continuous signal variance is available, whereas the second case considers system performance estimation when only the information about discretized signal variance is present, which means that there is a loss of input signal information. The main goal of such performance estimation comparison of the proposed speech signal coding model is to explore what is the objectivity of performance if the information about a continuous source is absent, which is a common phenomenon in digital systems. The advantages of the proposed coding scheme are demonstrated by comparing the performance of the reconstructed signal with other similar exiting speech signal coding systems.
- Forward adaptive quantization
- Quasi-logarithmic quantizer
- Speech coding
- Transform coding