Neuro-Steered Music Source Separation With EEG-Based Auditory Attention Decoding And Contrastive-NMF

Cantisani, Giorgia; Essid, Slim; Richard, Gaël

doi:10.1109/icassp39728.2021.9413841

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…In this paper, we focused on TSE, but similar extraction problems have also been explored for other audio-processing tasks. For example, much research has been performed on extracting the track of an instrument in a piece of music conditioned on, e.g., the type of instrument [62], video of the musician playing [63], or EEG signal of the listener [64]. These approaches may be important to realize, e.g., audio-visual music analysis [65].…”

Section: Beyond Speechmentioning

confidence: 99%

Neural Target Speech Extraction: An Overview

Žmolíková¹,

Delcroix²,

Ochiai³

et al. 2023

Preprint

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Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail-party effect. For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and non-target speech signals share similar characteristics, complicating their discrimination. Target speech/speaker extraction (TSE) isolates the speech signal of a target speaker from a mixture of several speakers with or without noises and reverberations using clues that identify the speaker in the mixture. Such clues might be a spatial clue indicating the direction of the target speaker, a video of the speaker's lips, or a pre-recorded enrollment utterance from which their voice characteristics can be derived. TSE is an emerging field of research that has received increased attention in recent years because it offers a practical approach to the cocktailparty problem and involves such aspects of signal processing as audio, visual, array processing, and deep learning. This paper focuses on recent neural-based approaches and presents an in-depth overview of TSE. We guide readers through the different major approaches, emphasizing the similarities among frameworks and discussing potential future directions.

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Section: Beyond Speechmentioning

confidence: 99%

Neural Target Speech Extraction: An Overview

Žmolíková¹,

Delcroix²,

Ochiai³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In fact, if the hearing aid was able to "know" which audio source the user is attending to, then it should be able to selectively enhance it. Therefore, combining AAD algorithms and hearing aids technologies, should lead to nextgeneration hearing aids allowing good performances in complex (or noisy) auditory environments (see for example: Das et al, 2016Das et al, , 2020Van Eyndhoven et al, 2017;Cantisani et al, 2020;Geirnaert et al, 2020).…”

Section: Application In Neuro-steered Hearing Aidsmentioning

confidence: 99%

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

2022

Frontiers Research Topics

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Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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“…Nonnegative matrix factorization (NMF) algorithms have been advanced for the application fields of bioinformatics, artificial intelligence [ 1 ], signal processing systems [ 2 ], and music signal processing systems [ 3 ]. Lee and Seung [ 4 ] formulated a parts-based illustrated algorithm to solve the problem of the NMF puzzle.…”

Section: Introductionmentioning

confidence: 99%

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

Güven¹

2023

JMIR Bioinform Biotech

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Background There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. The recent utilization of nonnegative matrix factorization (NMF) in computational biology has demonstrated the capability to derive essential details from a high amount of data in particular gene expression microarrays. A common problem in NMF is finding the proper number rank (r) of factors of the degraded demonstration, but no agreement exists on which technique is most appropriate to utilize for this purpose. Thus, various techniques have been suggested to select the optimal value of rank factorization (r). Objective In this work, a new metric for rank selection is proposed based on the elbow method, which was methodically compared against the cophenetic metric. Methods To decide the optimum number rank (r), this study focused on the unit invariant knee (UIK) method of the NMF on gene expression data sets. Since the UIK method requires an extremum distance estimator that is eventually employed for inflection and identification of a knee point, the proposed method finds the first inflection point of the curvature of the residual sum of squares of the proposed algorithms using the UIK method on gene expression data sets as a target matrix. Results Computation was conducted for the UIK task using gene expression data of acute lymphoblastic leukemia and acute myeloid leukemia samples. Consequently, the distinct results of NMF were subjected to comparison on different algorithms. The proposed UIK method is easy to perform, fast, free of a priori rank value input, and does not require initial parameters that significantly influence the model’s functionality. Conclusions This study demonstrates that the elbow method provides a credible prediction for both gene expression data and for precisely estimating simulated mutational processes data with known dimensions. The proposed UIK method is faster than conventional methods, including metrics utilizing the consensus matrix as a criterion for rank selection, while achieving significantly better computational efficiency without visual inspection on the curvatives. Finally, the suggested rank tuning method based on the elbow method for gene expression data is arguably theoretically superior to the cophenetic measure.

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Neuro-Steered Music Source Separation With EEG-Based Auditory Attention Decoding And Contrastive-NMF

Cited by 7 publications

References 25 publications

Neural Target Speech Extraction: An Overview

Neural Target Speech Extraction: An Overview

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

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