Independent Vector Analysis with Deep Neural Network Source Priors

Li, Xi-Lin

doi:10.48550/arxiv.2008.11273

Cited by 1 publication

(1 citation statement)

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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%