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Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG. ...
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Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.
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Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG
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In: Brain Lang (2021)
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Cortical Tracking of a Background Speaker Modulates the Comprehension of a Foreground Speech Signal
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In: J Neurosci (2021)
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Transcranial alternating current stimulation in the theta band but not in the delta band modulates the comprehension of naturalistic speech in noise
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Transcranial Alternating Current Stimulation With the Theta-Band Portion of the Temporally-Aligned Speech Envelope Improves Speech-in-Noise Comprehension
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In: Front Hum Neurosci (2020)
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Comparison of effects on subjective intelligibility and quality of speech in babble for two algorithms: A deep recurrent neural network and spectral subtraction. ...
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Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants. ...
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Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants
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Evaluation of a multi-channel algorithm for reducing transient sounds. ...
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Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality. ...
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Evaluation of a multi-channel algorithm for reducing transient sounds.
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Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.
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