Abstract
The performance of the independent vector analysis (IVA) algorithm depends on the choice of the source prior to better model the speech signals as it employs a multivariate source prior to retain the dependency between frequency bins of each source. Identical source priors are frequently used for the IVA methods; however, different speech sources will generally have different statistical properties. In this work, instead of identical source priors, a novel Student’s t mixture model based source prior is introduced for the IVA algorithm that can adapt to the statistical properties of different speech sources and thereby enhance the separation performance of the IVA algorithm. The unknown parameters of the source prior and unmixing matrices are estimated together by deriving an efficient expectation maximization (EM) algorithm. Useful improvement in the separation performance in different realistic scenarios is confirmed by experimental studies on real datasets.
Highlights
The process of automated separation of acoustic sources from measured mixtures is known as acoustic blind source separation (BSS) [1]
The new framework for the independent vector analysis (IVA) algorithm is tested in a simulated environment and in order to evaluate the performance in real scenarios, it is tested with real room impulse responses (RIRs), which can depict the performance of the proposed method in changing realistic settings
The separation performance for the proposed method was tested with image room impulse method and it confirms the advantage of using the proposed framework for the IVA method as it shows the Signal to Distortion Ratio (SDR) improvement of approximately 1 dB as compared to the original IVA method
Summary
The process of automated separation of acoustic sources from measured mixtures is known as acoustic blind source separation (BSS) [1]. The typical application of blind source separation is to handle the cocktail party problem, which is the process of focusing on one particular acoustic source of interest in the presence of multiple sound sources [2,3,4]. In the past few decades, much research has been conducted to study different aspects of the cocktail party problem. The ICA algorithm was proposed by Herault and Jutten [10,11]; it has limitations such as permutation and scaling problems [12,13,14]. The IVA algorithm is an extension of the ICA algorithm which was proposed to theoretically mitigate the permutation problem of the ICA method that is inherent to most of the BSS algorithms [15]
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