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.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.
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.
Most of the cochlear implant devices use envelope cues to provide electric stimulation.
Understanding the effect of various factors on melody recognition in the context of cochlear implants is important to improve the existing coding strategies.
In this dissertation, we propose a new hypothesis for the observed absence of release from masking by CI users.
Speech Coding Thesis
A new strategy is also developed and integrated into existing CI systems to improve speech recognition in noise for CI users.A selective compression algorithm, with estimated landmarks, was incorporated into the CI strategy, and tested by presenting the processed stimuli to CI users.Significant benefits were observed compared to performance obtained with the unprocessed noisy speech.Cochlear implant (CI) user’s performance degrades significantly in noisy environments, especially in non-steady noisy conditions.Unlike normal hearing listeners, CI users generally perform better when listening to speech in steady-state noise than in fluctuating maskers, and the reasons for that are unclear.Music perception and speech perception in noisy listening conditions with cochlear implants are still highly challenging problems.Many research studies have reported low recognition scores in the task of simple melody recognition.IEEE sentences containing clean obstruent segments, but corrupted (by steady noise or fluctuating maskers) sonorant segments (e.g., vowels) were presented to CI users.Results indicated that cochlear implant users received a substantial gain in intelligibility when they had access to the acoustic landmarks provided by obstruent consonants.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.