Scalable Parametric Audio Coder Using Sparse Approximation with Frame-to-Frame Perceptually Optimized Wavelet Packet Based Dictionary
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A. Petrovsky, V. Herasimovich, and A. Petrovsky, "Scalable Parametric Audio Coder Using Sparse Approximation with Frame-to-Frame Perceptually Optimized Wavelet Packet Based Dictionary," Paper 9264, (2015 May.). doi:
A. Petrovsky, V. Herasimovich, and A. Petrovsky, "Scalable Parametric Audio Coder Using Sparse Approximation with Frame-to-Frame Perceptually Optimized Wavelet Packet Based Dictionary," Paper 9264, (2015 May.). doi:
Abstract: This paper is devoted to the development of a scalable parametric audio coder based on a matching pursuit algorithm with a frame-based psychoacoustic optimized wavelet packet dictionary. The main idea is to parameterize audio signal with a minimum number of non-negative elements. This can be done by applying sparse approximation such as matching pursuit algorithm. In contrast with current approaches in audio coding based on sparse approximation we introduce a model of dynamic dictionary forming for each frame of input audio signal individually based on wavelet packet decomposition and dynamic wavelet packet tree transformation with psychoacoustic model. Experimental results of developed encoder and comparison with modern popular audio encoders are provided.
@article{petrovsky2015scalable,
author={petrovsky, alexey and herasimovich, vadzim and petrovsky, alexander},
journal={journal of the audio engineering society},
title={scalable parametric audio coder using sparse approximation with frame-to-frame perceptually optimized wavelet packet based dictionary},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},}
@article{petrovsky2015scalable,
author={petrovsky, alexey and herasimovich, vadzim and petrovsky, alexander},
journal={journal of the audio engineering society},
title={scalable parametric audio coder using sparse approximation with frame-to-frame perceptually optimized wavelet packet based dictionary},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={this paper is devoted to the development of a scalable parametric audio coder based on a matching pursuit algorithm with a frame-based psychoacoustic optimized wavelet packet dictionary. the main idea is to parameterize audio signal with a minimum number of non-negative elements. this can be done by applying sparse approximation such as matching pursuit algorithm. in contrast with current approaches in audio coding based on sparse approximation we introduce a model of dynamic dictionary forming for each frame of input audio signal individually based on wavelet packet decomposition and dynamic wavelet packet tree transformation with psychoacoustic model. experimental results of developed encoder and comparison with modern popular audio encoders are provided.},}
TY - paper
TI - Scalable Parametric Audio Coder Using Sparse Approximation with Frame-to-Frame Perceptually Optimized Wavelet Packet Based Dictionary
SP -
EP -
AU - Petrovsky, Alexey
AU - Herasimovich, Vadzim
AU - Petrovsky, Alexander
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015
TY - paper
TI - Scalable Parametric Audio Coder Using Sparse Approximation with Frame-to-Frame Perceptually Optimized Wavelet Packet Based Dictionary
SP -
EP -
AU - Petrovsky, Alexey
AU - Herasimovich, Vadzim
AU - Petrovsky, Alexander
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015
AB - This paper is devoted to the development of a scalable parametric audio coder based on a matching pursuit algorithm with a frame-based psychoacoustic optimized wavelet packet dictionary. The main idea is to parameterize audio signal with a minimum number of non-negative elements. This can be done by applying sparse approximation such as matching pursuit algorithm. In contrast with current approaches in audio coding based on sparse approximation we introduce a model of dynamic dictionary forming for each frame of input audio signal individually based on wavelet packet decomposition and dynamic wavelet packet tree transformation with psychoacoustic model. Experimental results of developed encoder and comparison with modern popular audio encoders are provided.
This paper is devoted to the development of a scalable parametric audio coder based on a matching pursuit algorithm with a frame-based psychoacoustic optimized wavelet packet dictionary. The main idea is to parameterize audio signal with a minimum number of non-negative elements. This can be done by applying sparse approximation such as matching pursuit algorithm. In contrast with current approaches in audio coding based on sparse approximation we introduce a model of dynamic dictionary forming for each frame of input audio signal individually based on wavelet packet decomposition and dynamic wavelet packet tree transformation with psychoacoustic model. Experimental results of developed encoder and comparison with modern popular audio encoders are provided.
Authors:
Petrovsky, Alexey; Herasimovich, Vadzim; Petrovsky, Alexander
Affiliation:
Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus
AES Convention:
138 (May 2015)
Paper Number:
9264
Publication Date:
May 6, 2015Import into BibTeX
Subject:
Audio Signal Processing
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=17688