Low bit-rate speech coding based on an improved sinusoidal model

This paper addresses the design, implementation and evaluation of efficient low bit-rate speech coding algorithms based on an improved sinusoidal model. A series of algorithms were developed for speech classification and pitch frequency determination, modeling of sinusoidal amplitudes and phases, and frame interpolation. An improved paradigm for sinusoidal phase coding is presented, where short-time sinusoidal phases are modeled using a combination of linear prediction, spectral sampling, linear phase alignment and all-pass phase error correction components. A class-dependent split vector quantization scheme is used to encode the sinusoidal amplitudes. The masking properties of the human auditory system are effectively exploited in the algorithms. The algorithms were successfully integrated into a 2.4 kbps sinusoidal coder. The performance of the 2.4 kbps coder was evaluated in terms of informal subjective tests such as the mean opinion score (MOS) and the diagnostic rhyme test (DRT), as well as some perceptually motivated objective distortion measures. Performance analysis on a large speech database indicates considerable improvement in short-time signal matching both in the time and the spectral domains. In addition, subjective quality of the reproduced speech is considerably improved.