OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest. METHODS: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech envelope based on the EEG, and finally uses this envelope to inform a multichannel speech separation and denoising algorithm. RESULTS: Strong suppression of interfering (unattended) speech and background noise is achieved, while the attended speech is preserved. Furthermore, EEG-based auditory attention detection (AAD) is shown to be robust to the use of noisy speech signals. CONCLUSIONS: Our results show that AAD-based speaker extraction from microphone array recordings is feasible and robust, even in noisy acoustic environments, and without access to the clean speech signals to perform EEG-based AAD. SIGNIFICANCE: Current research on AAD always assumes the availability of the clean speech signals, which limits the applicability in real settings. We have extended this research to detect the attended speaker even when only microphone recordings with noisy speech mixtures are available. This is an enabling ingredient for new brain-computer interfaces and effective filtering schemes in neuro-steered hearing prostheses. Here, we provide a first proof of concept for EEG-informed attended speaker extraction and denoising.
OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest. METHODS: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech envelope based on the EEG, and finally uses this envelope to inform a multichannel speech separation and denoising algorithm. RESULTS: Strong suppression of interfering (unattended) speech and background noise is achieved, while the attended speech is preserved. Furthermore, EEG-based auditory attention detection (AAD) is shown to be robust to the use of noisy speech signals. CONCLUSIONS: Our results show that AAD-based speaker extraction from microphone array recordings is feasible and robust, even in noisy acoustic environments, and without access to the clean speech signals to perform EEG-based AAD. SIGNIFICANCE: Current research on AAD always assumes the availability of the clean speech signals, which limits the applicability in real settings. We have extended this research to detect the attended speaker even when only microphone recordings with noisy speech mixtures are available. This is an enabling ingredient for new brain-computer interfaces and effective filtering schemes in neuro-steered hearing prostheses. Here, we provide a first proof of concept for EEG-informed attended speaker extraction and denoising.
Authors: James O'Sullivan; Zhuo Chen; Jose Herrero; Guy M McKhann; Sameer A Sheth; Ashesh D Mehta; Nima Mesgarani Journal: J Neural Eng Date: 2017-08-04 Impact factor: 5.379
Authors: Daniel D E Wong; Søren A Fuglsang; Jens Hjortkjær; Enea Ceolini; Malcolm Slaney; Alain de Cheveigné Journal: Front Neurosci Date: 2018-08-07 Impact factor: 4.677
Authors: Enea Ceolini; Jens Hjortkjær; Daniel D E Wong; James O'Sullivan; Vinay S Raghavan; Jose Herrero; Ashesh D Mehta; Shih-Chii Liu; Nima Mesgarani Journal: Neuroimage Date: 2020-08-20 Impact factor: 6.556
Authors: Sina Miran; Sahar Akram; Alireza Sheikhattar; Jonathan Z Simon; Tao Zhang; Behtash Babadi Journal: Front Neurosci Date: 2018-05-01 Impact factor: 4.677