Audio object coding based on optimal parameter frequency resolution

Wu, Tingzhao; Hu, Ruimin; Wang, Xiaochen; Ke, Shanfa

doi:10.1007/s11042-019-7409-7

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…Two representative approaches [10] (NMF-SAOC) and [11] (SVD-SAOC) are used as baselines. For comparability, downmix signal in all approaches is compressed by MEPG AAC codec at 128 kbps.…”

Section: Evaluations and Resultsmentioning

confidence: 99%

“…The sparsity of objects will change with the audio categories and the number of audio objects in practical application. Parameters-based approaches use dimensionality reduction algorithms to compress parameters [9][10][11]. Considering that each frame of the SAOC framework is divided into 28 sub-bands, all frequency points in one sub-band share the common parameter.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce aliasing distortion, Wu et al [10] divided the sub-bands from 85 to 242 and introduced the non-negative matrix factorization (NMF) method to reduce the bitrate caused by high resolution (called NMF-SAOC). By analyzing how the signal distortion varies with parameter frequency resolution, [11] used singular value decomposition (SVD) to reduce the dimensionality of transmitting parameters under finely divided sub-bands (called SVD-SAOC), whose strategy is to divide the sub-bands into 112.…”

Section: Introductionmentioning

confidence: 99%

“…Schematic diagram of state-of-the-art methods, the black parts represent the conventional SAOC approach[3]. The black parts plus green parts denote multi-step SAOC[4,5], and black parts plus red parts represent NMF-SAOC[10] and SVD-SAOC[11], respectively.…”

mentioning

confidence: 99%

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Low Bitrates Audio Object Coding Using Convolutional Auto-Encoder and Densenet Mixture Model

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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The efficient transmission of the audio objects can be achieved by spatial audio object coding (SAOC) method that conveys a mono downmix signal together with side information parameters that enable object reconstruction in the decoder. To allow the transmission of audio objects at low bitrates, we present a new audio coding method with convolutional auto-encoder (CAE) and dense convolutional network (DenseNet) mixture model, optimizing the compression of side information parameters of audio objects. It has two main advantages: 1) Different from the linear transform methods, CAE can dig the nonlinear relationship of side information parameters and can effectively reduce the dimension of side information parameters; 2) DenseNet is adding in the decoder to make full use of low dimensional features of side information parameters, which improves the audio quality at low bitrate. Experiments show that our method outperforms baseline methods permitting bitrates as low as 1 kbps per object.

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“…Two representative approaches [10] (NMF-SAOC) and [11] (SVD-SAOC) are used as baselines. For comparability, downmix signal in all approaches is compressed by MEPG AAC codec at 128 kbps.…”

Section: Evaluations and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Low Bitrates Audio Object Coding Using Convolutional Auto-Encoder and Densenet Mixture Model

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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“…Tingzhao Wu et al reduced the aliasing distortion of SAOC by a higher resolution subband partition method and applied matrix factorization to reduce the increasing bit-rate caused by division [14]. Furthermore, they analyzed how the object distortion varies with subband frequency resolution and found the optimal resolution to reduce aliasing distortion [15]. Tianshu Qu et al encoded audio objects via sparse auto-encoder.…”

Section: Related Workmentioning

confidence: 99%

Efficient Multi-Step Audio Object Coding with Limited Residual Information

Wang

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. Audio systems can provide personalized services under this framework. However, this method causes frequency aliasing distortion, which severely impacts the listening experience. The multi-step SAOC (MS-SAOC) scheme was proposed to enhance the sound quality of each audio object by using residual information. Compared with SAOC, the bit-rate increases three times due to the residual data of multiple objects. In this paper, an efficient multistep residual coding method is proposed to reduce the residual bit-rate of MS-SAOC. A two-level filter is designed to remove redundant residual information, and the limited residual information can efficiently compensate for frequency aliasing distortion. From experiment results, the residual bit-rate is half of MS-SAOC, and the sound quality is maintained at the Good-Excellent level.

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Multi-step Coding Structure of Spatial Audio Object Coding

Wang

et al. 2019

MultiMedia Modeling

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Audio object coding based on optimal parameter frequency resolution

Cited by 12 publications

References 21 publications

Low Bitrates Audio Object Coding Using Convolutional Auto-Encoder and Densenet Mixture Model

Low Bitrates Audio Object Coding Using Convolutional Auto-Encoder and Densenet Mixture Model

Efficient Multi-Step Audio Object Coding with Limited Residual Information

Multi-step Coding Structure of Spatial Audio Object Coding

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