System and method for multiresolution scalable audio signal encoding

TLDR: In this article, the authors proposed a model that considers audio signals to be composed of deterministic or sinusoidal components, transient components representing the onset of notes or other events in an audio signal, and stochastic components.

Abstract: An audio signal analyzer and encoder is based on a model that considers audio signals to be composed of deterministic or sinusoidal components, transient components representing the onset of notes or other events in an audio signal, and stochastic components. Deterministic components are represented as a series of overlapping sinusoidal waveforms. To generate the deterministic components, the input signal is divided into a set of frequency bands by a multi-complementary filter bank. The frequency band signals are oversampled so as to suppress cross-band aliasing energy in each band. Each frequency band is analyzed and encoded as a set of spectral components using a windowing time frame whose length is inversely proportional to the frequency range in that band. Low frequency bands are encoded using longer time frames than higher frequency bands. Transient components are represented by parameters denoting sinusoidal shaped waveforms produced when the transient components are transformed into a real valued frequency domain waveform. Stochastic or noise components are represented as a series of spectral envelopes. The parameters representing the three signal components compose a stream of compressed encoded audio data that can be further compressed so as to meet a specified transmission bandwidth limit by the deleting the least significant bits of quantized parameter values, reducing the update rates of parameters, and/or deleting the parameters used to encode higher frequency bands until the bandwidth of the compressed audio data meets the bandwidth requirement. Signal quality degrades in a graduated manner with successive reductions in the transmitted data rate.