End-to-End Optimization of Source Models for Speech and Audio Coding Using a Machine Learning Framework

Abstract

Speech coding is the most commonly used application of speech processing. Accumulated layers of improvements have however made codecs so complex that optimization of individual modules becomes increasingly difficult. This work introduces machine learning methodology to speech and audio coding, such that we can optimize quality in terms of overall entropy. We can then use conventional quantization, coding and perceptual models without modification such that the codec adheres to conventional requirements on algorithmic complexity, latency and robustness to packet loss. Experiments demonstrate that end-to-end optimization of quantization accuracy of the spectral envelope can be used for a lossless reduction in bitrate of 0.4 kbits/s.