Abstract
Noise reduction is often essential for cochlear implant (CI) recipients to achieve acceptable speech perception in noisy environments. Most noise reduction algorithms applied to audio signals are based on time-frequency representations of the input, such as the Fourier transform. Algorithms based on other representations may also be able to provide comparable or improved speech perception and listening quality improvements. In this paper, a noise reduction algorithm for CI sound processing is proposed based on the wavelet transform. The algorithm uses a dual-tree complex discrete wavelet transform followed by shrinkage of the wavelet coefficients based on a statistical estimation of the variance of the noise. The proposed noise reduction algorithm was evaluated by comparing its performance to those of many existing wavelet-based algorithms. The speech transmission index (STI) of the proposed algorithm is significantly better than other tested algorithms for the speech-weighted noise of different levels of signal to noise ratio. The effectiveness of the proposed system was clinically evaluated with CI recipients. A significant improvement in speech perception of 1.9 dB was found on average in speech weighted noise.
Highlights
Quiet listening environment, modern cochlear implant (CI) devices are capable of restoring speech perception to most recipients [1]
The objective of this study is to investigate the clinical application of the wavelet transform as a tool in noise reduction for CI recipients
The clinical results of the DTDWT in babble noise conditions showed no significant improvement over the ACE condition
Summary
Quiet listening environment, modern cochlear implant (CI) devices are capable of restoring speech perception to most recipients [1]. Noisy listening environment, the level of speech perception degrades rapidly with the increased noise level [2]. Studies evaluating single channel noise reduction algorithms for CIs have reported significant speech perception improvements of 24 percentage points in babble noise [2], 2.1 dB speech reception thresholds [3] and 19 percentage points [4] in speech weighted noise. This work is inspired by the improvement of speech understanding due to noise reduction techniques. We aim to explore different ways to further improve speech understanding in noisy listening environments. We briefly review noise reduction techniques related to our work
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