Neural network based arithmetic coding for real-time audio transmission on the TMS320C6000 DSP platform

We developed a real-time wideband speech codec adopting a wavelet packet based methodology. The transform domain coefficients were first quantized by using a psycho-acoustic model and then encoded with an arithmetic coding. The arithmetic coding was carried out by adapting the probability model of the quantized coefficients frame by frame by means of a competitive neural network, which was trained to detect regularities in the distribution of the wavelet packet coefficients. The weight matrix of the neural network is periodically updated during the compression in order to model better the speech characteristics of the current speakers. The coding/decoding algorithm was first written in C and then optimized on the TMS320C6000 DSP platform in a QoS-compliant fashion.