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- W4385328181 abstract "Humans possess the remarkable ability to selectively attend to a single speaker amidst competing voices and background noise, known as selective auditory attention. Recent studies in auditory neuroscience indicate a strong correlation between the attended speech signal and the corresponding brain's elicited neuronal activities, which the latter can be measured using affordable and non-intrusive electroencephalography (EEG) devices. In this study, we present NeuroHeed, a speaker extraction model that leverages EEG signals to establish a neuronal attractor which is temporally associated with the speech stimulus, facilitating the extraction of the attended speech signal in a cocktail party scenario. We propose both an offline and an online NeuroHeed, with the latter designed for real-time inference. In the online NeuroHeed, we additionally propose an autoregressive speaker encoder, which accumulates past extracted speech signals for self-enrollment of the attended speaker information into an auditory attractor, that retains the attentional momentum over time. Online NeuroHeed extracts the current window of the speech signals with guidance from both attractors. Experimental results demonstrate that NeuroHeed effectively extracts brain-attended speech signals, achieving high signal quality, excellent perceptual quality, and intelligibility in a two-speaker scenario." @default.
- W4385328181 created "2023-07-28" @default.
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- W4385328181 date "2023-07-26" @default.
- W4385328181 modified "2023-10-14" @default.
- W4385328181 title "NeuroHeed: Neuro-Steered Speaker Extraction using EEG Signals" @default.
- W4385328181 doi "https://doi.org/10.48550/arxiv.2307.14303" @default.
- W4385328181 hasPublicationYear "2023" @default.
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