Signal-adaptive Parametric Modelling for High Quality Low Bit Rate Audio Coding

Reche-Lopez PJ, Vera-Candeas P, Ruiz-Reyes N, Curpian-Alonso J, Rosa-Zurera M. Signal-adaptive Parametric Modelling for High Quality Low Bit Rate Audio Coding. In: AES Convention 116. Audio Engineering Society; 2004. Paper 6176. Available from: https://aes.org/publications/elibrary-page/?id=12753

@inproceedings{RecheLopez2004_12753,
  author = {Reche-Lopez, Pedro Jesus and Vera-Candeas, Pedro and Ruiz-Reyes, Nicolas and Curpian-Alonso, Jose and Rosa-Zurera, Manuel},
  title = {{Signal-adaptive Parametric Modelling for High Quality Low Bit Rate Audio Coding}},
  booktitle = {AES Convention 116},
  note = {Paper 6176},
  year = {2004},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12753}
}

TY  - CPAPER
TI  - Signal-adaptive Parametric Modelling for High Quality Low Bit Rate Audio Coding
AU  - Reche-Lopez, Pedro Jesus
AU  - Vera-Candeas, Pedro
AU  - Ruiz-Reyes, Nicolas
AU  - Curpian-Alonso, Jose
AU  - Rosa-Zurera, Manuel
T2  - AES Convention 116
M1  - Paper 6176
PY  - 2004
DA  - 2004/05/06
UR  - https://aes.org/publications/elibrary-page/?id=12753
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, signal-adaptive parametric models based on overcomplete dictionaries of time-frequency atoms are considered for high quality low bit-rate parametric audio coding. There are a variety of frameworks for deriving overcomplete signal expansions, which differ in the structure of the dictionary and the manner in which dictionary atoms are selected for the expansion. Psychoacoustic-adapted matching pursuits are accomplished for extracting sinusoidal components using an harmonic dictionary, while energy-adapted matching pursuits are carried out for transients modelling with a wavelet-based dictionary. First, transients are detected, modelled (with wavelet functions) and removed from the original audio signal, leaving a residue. Then, sinusoids are modelled using complex exponential functions and removed from the initial residue, leaving a noise-like signal. This final residue is modelled taking advantage of the good time-frequency location of the wavelet transform and considering psychoacoustic principles. An M-depth Wavelet Transform is first applied to the residue. Energy of each wavelet sub-band is then computed, and finally a Time Noise Shaping (TNS) process is applied to each one, which involves a parametric model for the noise-like signal. The resulting multi-part model (Sines + Transients + Noise) is efficiently applied by taking into account psycho-acoustical information for audio coding purposes. The combination of these all ideas results in nearly transparent parametric audio coding at binary rates close to 16kbps for most of the CD-quality one channel audio signals considered for testing. Listening tests allow us to say that our coder achieves better results than MPEG-4 AAC at very low bit rates (close to 16kbps).
ER  -