High Parameter Frequency Resolution Encoding Scheme for Spatial Audio Objects Using Stacked Sparse Autoencoder
Object-based audio systems have become common in recent years as they provide the flexibility for many auditory scenarios, such as virtual reality games, interactive theater, and spatial audio communication. For saving bitrates, multiple audio objects are compressed into a mono downmix signal and si...
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01.04.2022
Springer Nature B.V |
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| ISSN: | 1370-4621, 1573-773X |
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| Abstract | Object-based audio systems have become common in recent years as they provide the flexibility for many auditory scenarios, such as virtual reality games, interactive theater, and spatial audio communication. For saving bitrates, multiple audio objects are compressed into a mono downmix signal and side information parameters. However, side information parameter frequency resolution is too low to cause aliasing distortion. To overcome this issue, a new encoding scheme based on high parameter frequency resolution (224 sub-bands in a frame) is proposed in this paper. The side information parameters with high frequency resolution are compressed and reconstructed via SSAE (stacked sparse autoencoder) neural network and further used for recovering the audio objects. The performance of the proposed method is compared against existing SAOC (spatial audio object coding) methods at the same overall bitrate, judged by both objective and subjective results. The evaluation shows that our approach can facilitate the high quality of spatial audio objects. |
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| AbstractList | Object-based audio systems have become common in recent years as they provide the flexibility for many auditory scenarios, such as virtual reality games, interactive theater, and spatial audio communication. For saving bitrates, multiple audio objects are compressed into a mono downmix signal and side information parameters. However, side information parameter frequency resolution is too low to cause aliasing distortion. To overcome this issue, a new encoding scheme based on high parameter frequency resolution (224 sub-bands in a frame) is proposed in this paper. The side information parameters with high frequency resolution are compressed and reconstructed via SSAE (stacked sparse autoencoder) neural network and further used for recovering the audio objects. The performance of the proposed method is compared against existing SAOC (spatial audio object coding) methods at the same overall bitrate, judged by both objective and subjective results. The evaluation shows that our approach can facilitate the high quality of spatial audio objects. |
| Author | Ke, Shanfa Hu, Ruimin Wang, Xiaochen Wu, Yulin Hu, Chenhao |
| Author_xml | – sequence: 1 givenname: Yulin surname: Wu fullname: Wu, Yulin organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University – sequence: 2 givenname: Ruimin orcidid: 0000-0002-5872-3872 surname: Hu fullname: Hu, Ruimin email: hrm@whu.edu.cn organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University – sequence: 3 givenname: Xiaochen surname: Wang fullname: Wang, Xiaochen organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Research Institute of Wuhan University in Shenzhen – sequence: 4 givenname: Chenhao surname: Hu fullname: Hu, Chenhao organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University – sequence: 5 givenname: Shanfa surname: Ke fullname: Ke, Shanfa organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University |
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| SubjectTerms | Algorithms Artificial Intelligence Audio data Coding Complex Systems Computational Intelligence Computer Science Data compression Methods Neural networks Parameters Sparsity Virtual reality |
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| Title | High Parameter Frequency Resolution Encoding Scheme for Spatial Audio Objects Using Stacked Sparse Autoencoder |
| URI | https://link.springer.com/article/10.1007/s11063-021-10659-8 https://www.proquest.com/docview/2918343261 |
| Volume | 54 |
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