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In the current work we investigate the use of new filter spacing techniques called ‘Semitone filter spacing techniques’ in which filter bandwidths are varied in correspondence to the musical semitone steps.Noise reduction methods investigated so far for use with cochlear implants are mostly pre-processing methods.
In this dissertation we investigate the use of two such embedded noise reduction methods namely, ‘SNR weighting method’ and ‘S-shaped compression’ to improve speech perception in noisy listening conditions.
SNR weighting noise reduction method is an exponential weighting method that uses the instantaneous signal to noise ratio (SNR) estimate to perform noise reduction in each frequency band that corresponds to a particular electrode in the cochlear implant.
To detect the landmarks used in the selective compression strategy, automatic consonant-landmark detection algorithms were developed to handle adverse speech conditions.
High accuracy detection rate was achieved using machine learning algorithms.
In these methods, the speech signal is first enhanced using the noise reduction method and the enhanced signal is then processed using the speech processor.
A better and more efficient approach is to integrate the noise reduction mechanism into the cochlear implant signal processing.
To test this hypothesis, normal-hearing (NH) listeners are presented with vocoded sentences containing clean obstruent segments, but corrupted (by steady noise or fluctuating maskers) sonorant segments (e.g., vowels).
Results indicated that NH listeners performed better with fluctuating maskers than with steady noise.
Since their inception in early 1970s cochlear implants have gradually gained popularity and consequently considerable research has been done to advance and improve the cochlear implant technology.
Most of the research conducted so far in the field of cochlear implants has been primarily focused on improving speech perception in quiet.