Speech Separation Using Independent Vector Analysis with an Amplitude Variable Gaussian Mixture Model

Gu, Zhaoyi; Lü, Jing; Chen, Kai

doi:10.21437/interspeech.2019-2076

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…In 2019, Gu et al [ 56 ] proposed a Gaussian mixture model IVA algorithm with time-varying parameters to accommodate temporal power fluctuations embedded in nonstationary speech signals, thus avoiding the pretraining process of the original Gaussian mixture model IVA (GMM-IVA) algorithm and using the corresponding improved EM algorithm to estimate the separation matrix and signal model. The experimental results confirm the effectiveness of the method in random initialization and the advantages in separation accuracy and convergence speed.…”

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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“…For example, proximal splitting allows for a versatile algorithm with a heuristic extension based on masking [18], [19]. Another approach, specialized for two sources, is based on expectationmaximization and a Gaussian mixture model [20].…”

Section: Introductionmentioning

confidence: 99%

Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization

Scheibler

2020

Preprint

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We propose a new algorithm for blind source separation of convolutive mixtures using independent vector analysis. This is an improvement over the popular auxiliary function based independent vector analysis (AuxIVA) with iterative projection (IP) or iterative source steering (ISS). We introduce iterative projection with adjustment (IPA), whereas we update one demixing filter and jointly adjust all the other sources along its current direction. We implement this scheme as multiplicative updates by a rank-2 perturbation of the identity matrix. Each update involves solving a non-convex minimization problem that we term log-quadratically penalized quadratic minimization (LQPQM), that we think is of interest beyond this work. We find that the global minimum of an LQPQM can be efficiently computed. In the general case, we show that all its stationary points can be characterized as zeros of a kind of secular equation, reminiscent of modified eigenvalue problems. We further prove that the global minimum corresponds to the largest of these zeros. We propose a simple procedure based on Newton-Raphson seeded with a good initial point to efficiently compute it. We validate the performance of the proposed method for blind acoustic source separation via numerical experiments with reverberant speech mixtures. We show that not only is the convergence speed faster in terms of iterations, but each update is also computationally cheaper. Notably, for four and five sources, AuxIVA with IPA converges more than twice as fast as competing methods.

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Multichannel KHMF for speech separation with enthalpy based DOA and score based CNN (SCNN)

Koteswararao

Rao

2022

Evolving Systems

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Speech Separation Using Independent Vector Analysis with an Amplitude Variable Gaussian Mixture Model

Cited by 6 publications

References 24 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

Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization

Multichannel KHMF for speech separation with enthalpy based DOA and score based CNN (SCNN)

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