Independent Vector Analysis Exploiting Pre-learned Banks of Relative Transfer Functions for Assumed Target’s Positions

We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised by a pilot signal related to the source of interest (SOI), which ensures that the method correctly extracts the utterance of the desired speaker. The pilot is based on the identification of a dominant speaker in the mixture using x-vectors. The properties of the x-vectors computed in the presence of cross-talk are experimentally analyzed. The proposed approach is verified in a scenario with a moving SOI, static interfering speaker and environmental noise.

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“…SOI has been exploited in [5], additional audio measurements in the target area were used in [6], or video information was used in [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Blind Audio Source Extraction Supervised By Dominant Speaker Identification Using X-Vectors

Janský

Málek

Cmejla

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The contrast function for CSV is given by (20). Comparing (20) with (13) and using the same approach and assumptions to derive (42), we obtain the auxiliary function for the CSV model in the form…”

Section: Block Auxivementioning

confidence: 99%

“…Similarly, a video-based lip-movement detection was considered in [41]. A video-independent solution was proposed in [42] using spatial information about the area in which the speaker is located. All these approaches have been shown useful although the pilot signals used in them contain residual noise and interference.…”

Section: Piloted Block Auxivementioning

confidence: 99%

Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker

Janský¹,

Koldovsky²,

Málek³

et al. 2020

Preprint

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Independent Vector Extraction (IVE) is a modification of Independent Vector Analysis (IVA) for Blind Source Extraction (BSE) to a setup in which only one source of interest (SOI) should be separated from a mixture of signals observed by microphones. The fundamental assumption is that the SOI is independent of the other signals. IVE shows reasonable results; however, its basic variant is limited to static sources. To extract a moving source, IVE has recently been extended by considering the Constant Separating Vector (CSV) mixing model. It enables us to estimate a separating filter that extracts the SOI from a wider spatial area through which the source has moved. However, only slow gradient-based algorithms were proposed in the pioneering papers on IVE and CSV. In this paper, we experimentally verify the applicability of the CSV mixing model and propose new IVE methods derived by modifying the auxiliary function-based algorithm for IVA. Piloted Variants are proposed as well for the methods with partially controllable global convergence. The methods are verified under reverberant and noisy conditions using model-based as well as real-world acoustic impulse responses. They are also verified within the CHiME-4 speech separation and recognition challenge. The experiments corroborate the applicability of the CSV mixing model for the blind moving source extraction as well as the improved convergence of the proposed algorithms.

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“…Alternatively, a pilot signal can be introduced, which is related to the SOI and controls the convergence. A pilot based on the constrained location of the SOI has been exploited in [9], additional audio measurements in the area of SOI were used in [10]. Acquisition of the above described types of prior information is, however, challenging in practice.…”

Section: Introductionmentioning

confidence: 99%

Blind Extraction of Moving Audio Source in a Challenging Environment Supported by Speaker Identification Via X-Vectors

Málek

Janský

Kounovsky

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a novel approach for semi-supervised extraction of a moving audio source of interest (SOI) applicable in reverberant and noisy environments. The blind part of the method is based on independent vector extraction (IVE) and uses the recently proposed constant separating vector (CSV) mixing model. This model allows for changes of mixing parameters within the processed interval of the mixture, which potentially leads to higher accuracy of SOI estimation. The supervised part of the method concerns a pilot signal, which is related to the SOI and ensures the convergence of the blind method towards the SOI. The pilot is based on robust detection of frames where SOI is dominant via speaker embeddings called X-vectors. Robustness of the detection is achieved through augmentation of the data for the supervised training of the X-vectors. The pilot-supported extraction yields significantly better performance compared to its unsupervised counterpart identifying SOI solely using the initialization.

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Independent Vector Analysis Exploiting Pre-learned Banks of Relative Transfer Functions for Assumed Target’s Positions

Cited by 5 publications

References 17 publications

Adaptive Blind Audio Source Extraction Supervised By Dominant Speaker Identification Using X-Vectors

Adaptive Blind Audio Source Extraction Supervised By Dominant Speaker Identification Using X-Vectors

Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker

Blind Extraction of Moving Audio Source in a Challenging Environment Supported by Speaker Identification Via X-Vectors

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