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

Federico Bolner; Tobias Goehring; Jessica Monaghan; Bas Van Dijk; Jan Wouters; Stefan Bleeck

doi:10.1109/ICASSP.2016.7472933

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

Federico Bolner^*, Tobias Goehring, Jessica Monaghan, Bas Van Dijk, Jan Wouters, Stefan Bleeck

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

13 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	6520-6524
Number of pages	5
ISBN (Electronic)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472933
Publication status	Published - 18 May 2016
Externally published	Yes
Event	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016

Publication series

Name	International Conference on Acoustics Speech and Signal Processing ICASSP
Publisher	IEEE
ISSN (Print)	1520-6149

Other

Other	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country	China
City	Shanghai
Period	20/03/16 → 25/03/16

Keywords

Cochlear implants
machine learning
neural networks
noise reduction
speech enhancement

Access to Document

10.1109/ICASSP.2016.7472933

Link to publication in Scopus

Cite this

Bolner, F., Goehring, T., Monaghan, J., Van Dijk, B., Wouters, J., & Bleeck, S. (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 2016 - Proceedings (pp. 6520-6524). [7472933] (International Conference on Acoustics Speech and Signal Processing ICASSP). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2016.7472933

Bolner, Federico ; Goehring, Tobias ; Monaghan, Jessica ; Van Dijk, Bas ; Wouters, Jan ; Bleeck, Stefan. / Speech enhancement based on neural networks applied to cochlear implant coding strategies. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 6520-6524 (International Conference on Acoustics Speech and Signal Processing ICASSP).

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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.",

keywords = "Cochlear implants, machine learning, neural networks, noise reduction, speech enhancement",

author = "Federico Bolner and Tobias Goehring and Jessica Monaghan and {Van Dijk}, Bas and Jan Wouters and Stefan Bleeck",

year = "2016",

month = may,

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doi = "10.1109/ICASSP.2016.7472933",

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series = "International Conference on Acoustics Speech and Signal Processing ICASSP",

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Bolner, F, Goehring, T, Monaghan, J, Van Dijk, B, Wouters, J & Bleeck, S 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 2016 - Proceedings., 7472933, International Conference on Acoustics Speech and Signal Processing ICASSP, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 6520-6524, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 20/03/16. https://doi.org/10.1109/ICASSP.2016.7472933

Speech enhancement based on neural networks applied to cochlear implant coding strategies. / Bolner, Federico; Goehring, Tobias; Monaghan, Jessica; Van Dijk, Bas; Wouters, Jan; Bleeck, Stefan.

2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 6520-6524 7472933 (International Conference on Acoustics Speech and Signal Processing ICASSP).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Bolner, Federico

AU - Goehring, Tobias

AU - Monaghan, Jessica

AU - Van Dijk, Bas

AU - Wouters, Jan

AU - Bleeck, Stefan

PY - 2016/5/18

Y1 - 2016/5/18

N2 - 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.

AB - 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.

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BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings

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Y2 - 20 March 2016 through 25 March 2016

ER -

Bolner F, Goehring T, Monaghan J, Van Dijk B, Wouters J, Bleeck S. Speech enhancement based on neural networks applied to cochlear implant coding strategies. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 6520-6524. 7472933. (International Conference on Acoustics Speech and Signal Processing ICASSP). https://doi.org/10.1109/ICASSP.2016.7472933

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

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