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Speech enhancement based on neural networks applied to cochlear implant coding strategies

Speech enhancement based on neural networks applied to cochlear implant coding strategies
Speech enhancement based on neural networks applied to cochlear implant coding strategies
Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, particularly in the presence of a fluctuating masker. In the present study, a speech enhancement algorithm integrating an artificial neural network (NN) into CI coding strategies is proposed. The algorithm decomposes the noisy input signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the NN to produce an estimation of which CI channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is then used accordingly to retain a subset of channels for electrical stimulation, as in traditional n-of-m coding strategies. The proposed algorithm was tested with 10 normal-hearing participants listening to CI noise-vocoder simulations against a conventional Wiener filter based enhancement algorithm. Significant improvements in speech intelligibility in stationary and fluctuating noise were found over both unprocessed and Wiener filter processed conditions.
Cochlear implants, noise reduction, speech enhancement, neural networks, machine learning
6520-6524
IEEE
Bolner, Federico
37211af2-a077-42f7-aaec-21be5db4158d
Goehring, Tobias
0da30bba-a437-45f8-a817-898621066f28
Monaghan, Jessica
c6e0821f-a660-4f07-85ac-66033f0e0b44
van Dijk, Bas
d3a89823-6a40-4856-8f94-45e73f0cae3a
Wouters, Jan
03454b9c-b1fb-4b7d-adf9-a3cc73bb51e4
Bleeck, Stefan
c888ccba-e64c-47bf-b8fa-a687e87ec16c
Bolner, Federico
37211af2-a077-42f7-aaec-21be5db4158d
Goehring, Tobias
0da30bba-a437-45f8-a817-898621066f28
Monaghan, Jessica
c6e0821f-a660-4f07-85ac-66033f0e0b44
van Dijk, Bas
d3a89823-6a40-4856-8f94-45e73f0cae3a
Wouters, Jan
03454b9c-b1fb-4b7d-adf9-a3cc73bb51e4
Bleeck, Stefan
c888ccba-e64c-47bf-b8fa-a687e87ec16c

Bolner, Federico, Goehring, Tobias, Monaghan, Jessica, van Dijk, Bas, Wouters, Jan and Bleeck, Stefan (2016) Speech enhancement based on neural networks applied to cochlear implant coding strategies. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. pp. 6520-6524 . (doi:10.1109/ICASSP.2016.7472933).

Record type: Conference or Workshop Item (Paper)

Abstract

Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, particularly in the presence of a fluctuating masker. In the present study, a speech enhancement algorithm integrating an artificial neural network (NN) into CI coding strategies is proposed. The algorithm decomposes the noisy input signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the NN to produce an estimation of which CI channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is then used accordingly to retain a subset of channels for electrical stimulation, as in traditional n-of-m coding strategies. The proposed algorithm was tested with 10 normal-hearing participants listening to CI noise-vocoder simulations against a conventional Wiener filter based enhancement algorithm. Significant improvements in speech intelligibility in stationary and fluctuating noise were found over both unprocessed and Wiener filter processed conditions.

Full text not available from this repository.

More information

Accepted/In Press date: 21 December 2015
Published date: 16 May 2016
Keywords: Cochlear implants, noise reduction, speech enhancement, neural networks, machine learning
Organisations: Human Sciences Group, Faculty Hub

Identifiers

Local EPrints ID: 407126
URI: https://eprints.soton.ac.uk/id/eprint/407126
PURE UUID: 09de1572-2577-468d-9635-4357e2eb677c
ORCID for Stefan Bleeck: ORCID iD orcid.org/0000-0003-4378-3394

Catalogue record

Date deposited: 30 Mar 2017 01:05
Last modified: 14 Mar 2019 01:41

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