Frequency domain linear prediction for QMF sub-bands and applications to audio coding

  • Authors:

  • Petr Motlicek;Sriram Ganapathy;Hynek Hermansky;Harinath Garudadri

  • Affiliations:

  • IDIAP Research Institute, Martigny, Switzerland;IDIAP Research Institute, Martigny, Switzerland and École Polytechnique Fédérale de Lausanne, Switzerland;IDIAP Research Institute, Martigny, Switzerland and École Polytechnique Fédérale de Lausanne, Switzerland;Qualcomm Inc., San Diego, California

  • Venue:
  • MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
  • Year:
  • 2007

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Abstract

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 applied over relatively long time segments (e.g. 1000 ms). Line Spectral Frequency parameters related to autoregressive models are computed and quantized in each frequency sub-band. The sub-band residuals are quantized in the frequency domain using a combination of split Vector Quantization (VQ) (for magnitudes) and uniform scalar quantization (for phases). In the decoder, the sub-band signal is reconstructed using the quantized residual and the corresponding quantized envelope. Finally, application of inverse QMF reconstructs the audio signal. Even with simple quantization techniques and without any sophisticated modules, the proposed audio coder provides encouraging results in objective quality tests. Also, the proposed coder is easily scalable across a wide range of bit-rates.