Real-Time Independent Vector Analysis with a Deep-Learning-Based Source Model

Kang, Fang; Yang, Feiran; Yang, Jun

doi:10.1109/slt48900.2021.9383599

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Therefore, the selection of the source prior model determines whether the IVA algorithm can accurately capture the fine structure of the source signal, which plays a crucial role in the entire BSS process. In particular, the source prior model based on deep learning [ 19 ] and the source prior model based on deep neural network [ 20 ] are the focus of current source prior model research, and the source prior model is compared in detail in [ 20 ].…”

Section: Optimizing Iva Algorithm—optimizing Update Rulesmentioning

confidence: 99%

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

Guo

Luo

2023

Sensors

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With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive. It has become extremely important to extract useful signals by decomposing various mixed signals through blind source separation (BSS). BSS has been proven to have prominent applications in multichannel audio processing. For multichannel speech signals, independent component analysis (ICA) requires a certain statistical independence of source signals and other conditions to allow blind separation. independent vector analysis (IVA) is an extension of ICA for the simultaneous separation of multiple parallel mixed signals. IVA solves the problem of arrangement ambiguity caused by independent component analysis by exploiting the dependencies between source signal components and plays a crucial role in dealing with the problem of convolutional blind signal separation. So far, many researchers have made great contributions to the improvement of this algorithm by adopting different methods to optimize the update rules of the algorithm, accelerate the convergence speed of the algorithm, enhance the separation performance of the algorithm, and adapt to different application scenarios. This meaningful and attractive research work prompted us to conduct a comprehensive survey of this field. This paper briefly reviews the basic principles of the BSS problem, ICA, and IVA and focuses on the existing IVA-based optimization update rule techniques. Additionally, the experimental results show that the AuxIVA-IPA method has the best performance in the deterministic environment, followed by AuxIVA-IP2, and the OverIVA-IP2 has the best performance in the overdetermined environment. The performance of the IVA-NG method is not very optimistic in all environments.

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Section: Optimizing Iva Algorithm—optimizing Update Rulesmentioning

confidence: 99%

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

Guo

Luo

2023

Sensors

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“…The other parameters are set to β = 1 and L = 5. Four other algorithms are adopted for comparison, including two conventional methods (AuxIVA [8] and ILRMA [12]) and two DNN-based methods (IDLMA [13] and the method in [17] denoted as Kang method). The same DCCRN architecture and loss function are used for IDLMA and Kang method.…”

Section: Data Preparation and Experimental Setupmentioning

confidence: 99%

“…More recently, deep neural network (DNN) is utilized to model the source spectral characteristics [13,14,15,16,17,18] given its powerful modeling ability. In [13,14,15,16], the supervised learning of the source spectrogram is presented, which is further combined with the ILRMA-based blind estimation of the demixing matrix and is referred as independent deeply learned matrix analysis (IDLMA).…”

Section: Introductionmentioning

confidence: 99%

“…In [13,14,15,16], the supervised learning of the source spectrogram is presented, which is further combined with the ILRMA-based blind estimation of the demixing matrix and is referred as independent deeply learned matrix analysis (IDLMA). Due to the complexity of directly estimating the source spectrogram, the DNN-based supervised update of the source variance is then incorporated into the framework of IVA [17]. Taking advantage of the iterative source steering updates, the work in [18] proposes to determine the source model parameters by backing propagate the permutation invariant separation loss through multiple iterations of IVA.…”

Section: Introductionmentioning

confidence: 99%

“…Taking advantage of the iterative source steering updates, the work in [18] proposes to determine the source model parameters by backing propagate the permutation invariant separation loss through multiple iterations of IVA. These DNN models [13,14,15,16,17,18] can accurately estimate the parameters of the given source model. However, they need to update the source model parameters with DNN iteratively, leading to the expensive computational cost and the mismatch between training and testing data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Blind Source Separation Framework Towards Maximum Signal-To-Interference Ratio

Gu¹,

Cheng²,

Yao³

et al. 2021

Preprint

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In this paper, the optimal source model for the independent vector analysis (IVA) algorithm towards maximizing the output signal-to-interference ratio (SIR) is mathematically derived, and the corresponding optimal weighted covariance matrix is proved to be the covariance matrix of interference signals. A new algorithm framework called minimum variance IVA (MVIVA) is further proposed, where the deep neural network-based estimation of the interference covariance matrix is combined with the IVA-based estimation of the demixing matrix. Experimental results show the superiority of the proposed source model, and the MVIVA algorithm outperforms the original IVA algorithm by 9.6 dB in SIR and 5.8 dB in signal-to-distortion ration (SDR) on average.

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Faster independent vector analysis with joint pairwise updates of demixing vectors

Luo,

Guo,

Wang

2024

Artif Intell Rev

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Real-Time Independent Vector Analysis with a Deep-Learning-Based Source Model

Cited by 6 publications

References 18 publications

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

A Novel Blind Source Separation Framework Towards Maximum Signal-To-Interference Ratio

Faster independent vector analysis with joint pairwise updates of demixing vectors

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