| Autor: |
Federico Bolner, Jessica J. M. Monaghan, Marc Moonen, Tobias Goehring, Jan Wouters, Bas van Dijk, Stefan Bleeck |
| Rok vydání: |
2015 |
| Předmět: |
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| Zdroj: |
The Journal of the Acoustical Society of America. 138:1833-1833 |
| ISSN: |
0001-4966 |
| DOI: |
10.1121/1.4933832 |
| Popis: |
Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, especially in the presence of a fluctuating masker. Motivated by previous intelligibility studies of speech synthesized using the ideal binary mask, we propose a framework that integrates a multi-layer feed-forward artificial neural network (ANN) into CI coding strategies. The algorithm decomposes the noisy input signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the ANN to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, (SNR)). This estimate is then used accordingly to suppress the noise and retain the appropriate subset of channels for electrical stimulation, as in traditional N-of-M coding strategies. Speech corrupted by various noise types at different SNRs is processed by the algorithm and re-synthesized with a vocoder. Evaluation has been performed in comparison with the Advanced Combination Encoder (ACE) in terms of classification performance and objective intelligibility measures. Results indicated significant improvement in Hit—False Alarm rates and intelligibility prediction scores, especially in negative SNR conditions. Findings suggested that the use of ANNs could potentially improve speech intelligibility in noise for CI users and motivated subjective listening experiments that will be presented together with the objective results. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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