Wide-Band Audio Coding Based on Frequency-Domain Linear Prediction

Wide-Band Audio Coding Based on Frequency-Domain Linear Prediction

In this paper, we re-visit an original concept of speech coding in which the signal is separated into the carrier modulated by the signal envelope. A recently developed technique, called frequency domain linear prediction (FDLP), is applied for the efficient estimation of the envelope. The processing in the temporal domain allows for a straightforward emulation of the forward temporal masking. ...

Non-uniform QMF Decomposition for Wide-band Audio Coding based on Frequency Domain Linear Prediction

This paper presents a new technique for perfect reconstruction non-uniform QMF decomposition developed to increase efficiency of a generic wide-band audio coding system based on Frequency Domain Linear Prediction (FDLP). The base line FDLP codec, operating at high bit-rates (∼136 kbps), exploits an uniform QMF decomposition into 64 sub-bands followed by sub-band processing based on FDLP. Here, ...

Scalable Wide - band Audio Codec based on Frequency Domain Linear Prediction ( version 2 )

This paper proposes a technique for wide-band audio applications based on the predictability of the temporal evolution of Quadrature Mirror Filter (QMF) sub-band signals. An input audio signal is first decomposed into 64 sub-band signals using QMF decomposition. The temporal envelopes in critically sampled QMF sub-bands are approximated using frequency domain linear prediction applied over rela...

Frequency Domain Linear Prediction for QMF Sub-bands and Applications to Audio Coding

This paper proposes an analysis technique for wide-band audio applications based on the predictability of the temporal evolution of Quadrature Mirror Filter (QMF) sub-band signals. The input audio signal is first decomposed into 64 sub-band signals using QMF decomposition. The temporal envelopes in critically sampled QMF sub-bands are approximated using frequency domain linear prediction applie...

Frequency Domain Linear Prediction for Speech and Audio Applications