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S. Zhang, X. Wu, and T. Qu, "Sparse Autoencoder Based Multiple Audio Objects Coding Method," Paper 10172, (2019 March.). doi: S. Zhang, X. Wu, and T. Qu, "Sparse Autoencoder Based Multiple Audio Objects Coding Method," Paper 10172, (2019 March.). doi:
Abstract: The traditional multiple audio objects codec extracts the parameters of each object in the frequency domain and produces serious confusion because of high coincidence degree in subband among objects. This paper uses sparse domain instead of frequency domain and reconstruct audio object using the binary mask from the down-mixed signal based on the sparsity of each audio object. In order to overcome high coincidence degree of subband among different audio objects, the sparse autoencoder neural network is established. On this basis, a multiple audio objects codec system is built up. To evaluate this proposed system, the objective and subjective evaluation are carried on and the results show that the proposed system has the better performance than SAOC.

@article{zhang2019sparse,
author={zhang, shuang and wu, xihong and qu, tianshu},
journal={journal of the audio engineering society},
title={sparse autoencoder based multiple audio objects coding method},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},} @article{zhang2019sparse,
author={zhang, shuang and wu, xihong and qu, tianshu},
journal={journal of the audio engineering society},
title={sparse autoencoder based multiple audio objects coding method},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},
abstract={the traditional multiple audio objects codec extracts the parameters of each object in the frequency domain and produces serious confusion because of high coincidence degree in subband among objects. this paper uses sparse domain instead of frequency domain and reconstruct audio object using the binary mask from the down-mixed signal based on the sparsity of each audio object. in order to overcome high coincidence degree of subband among different audio objects, the sparse autoencoder neural network is established. on this basis, a multiple audio objects codec system is built up. to evaluate this proposed system, the objective and subjective evaluation are carried on and the results show that the proposed system has the better performance than saoc.},}

TY - paper
TI - Sparse Autoencoder Based Multiple Audio Objects Coding Method
SP - EP -
AU - Zhang, Shuang
AU - Wu, Xihong
AU - Qu, Tianshu
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019 TY - paper
TI - Sparse Autoencoder Based Multiple Audio Objects Coding Method
SP - EP -
AU - Zhang, Shuang
AU - Wu, Xihong
AU - Qu, Tianshu
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
AB - The traditional multiple audio objects codec extracts the parameters of each object in the frequency domain and produces serious confusion because of high coincidence degree in subband among objects. This paper uses sparse domain instead of frequency domain and reconstruct audio object using the binary mask from the down-mixed signal based on the sparsity of each audio object. In order to overcome high coincidence degree of subband among different audio objects, the sparse autoencoder neural network is established. On this basis, a multiple audio objects codec system is built up. To evaluate this proposed system, the objective and subjective evaluation are carried on and the results show that the proposed system has the better performance than SAOC.