title: Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.
abstract: Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices.
authors.affiliation: ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom. Electronic address: goehring.tobias@gmail.com.
authors.forename: Tobias
authors.initials: T
authors.lastname: Goehring
authors.affiliation: ExpORL, KU Leuven, O&N II Herestraat 49, 3000 Leuven, Belgium; Cochlear Technology Centre, Schaliënhoevedreef 20 I, 2800 Mechelen, Belgium.
authors.forename: Federico
authors.initials: F
authors.lastname: Bolner
authors.affiliation: ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom.
authors.forename: Jessica J M
authors.initials: JJ
authors.lastname: Monaghan
authors.affiliation: Cochlear Technology Centre, Schaliënhoevedreef 20 I, 2800 Mechelen, Belgium.
authors.forename: Bas
authors.initials: B
authors.lastname: van Dijk
authors.affiliation: European Institute for ORL-HNS, Sint Augustinus Hospital, Oosterveldlaan 24, 2610 Wilrijk, Belgium.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Zarowski
authors.affiliation: ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom.
authors.forename: Stefan
authors.initials: S
authors.lastname: Bleeck
mesh_terms: D000161:Acoustic Stimulation; D000162:Acoustics; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001302:Audiometry, Speech; D019929:Cochlear Implantation / Q000295:instrumentation*; D003054:Cochlear Implants; D032882:Comprehension; D004558:Electric Stimulation; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D009622:Noise / Q000009:adverse effects*; D010470:Perceptual Masking; D019986:Persons with Hearing Disabilities / Q000523:psychology / Q000534:rehabilitation*; D011474:Prosthesis Design; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013065:Speech Intelligibility; D013067:Speech Perception; D055815:Young Adult
keywords: Cochlear implants; Machine learning; Neural networks; Noise reduction; Speech enhancement