ArticleProceedings of meetings on acoustics. Acoustical Society of America2019
A Computationally efficient IVA-based Blind Source Separation for Hearing Aid Applications and its Real-time Implementation on Smartphone.
Article in Proceedings of meetings on acoustics. Acoustical Society of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many real-time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone in real-time to separate speech and noise in realistic scenarios for Hearing-Aid (HA) applications. The experimental results using objective and subjective tests reveal the usefulness of the developed method for real-world applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.