No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

<p>Cochlear implants (CIs) are devices that restore some level of hearing to deaf individuals. Because of their design and the impaired nature of the deafened auditory system, CIs provide listeners with limited spectral and temporal information, resulting in speech recognition that degrades more rapidly for CI listeners than for normal hearing listeners in noisy and reverberant environments (Kokkinakis and Loizou, 2011). This research project aimed to mitigate the effects of reverberation by directly manipulating the CI pulse train. A reverberation detection algorithm was initially developed to control processing when switching between the mitigation algorithm and a standard signal processing algorithm used when no mitigation is needed. Next, the benefit of removing two separate effects of reverberation was studied. Finally, two reverberation mitigation algorithms were developed. Because the two algorithms resulted in comparable performance, the effect of one algorithm on speech recognition was assessed in normal hearing (NH) and CI listeners. </p><p>Reverberation detection, which has not been thoroughly investigated in the CI literature, would provide a method to control the initiation of a reverberation mitigation algorithm. Although a mitigation algorithm would ideally remove reverberation without affecting non-reverberant signals, most noise and reverberation mitigation algorithms make errors and should only be applied when necessary. Therefore, a reverberation detection algorithm was designed to control the reverberation mitigation algorithm and thereby reduce unnecessary processing. The detection algorithm was implemented by first developing features from the frequency-time matrices that result from the standard CI speech processing algorithm. Next, using these features, a maximum a posteriori classifier was shown to successfully discriminate speech in quiet, reverberation, speech shaped noise, and white Gaussian noise with 94% accuracy.</p><p>In order to develop the mitigation algorithm that would be controlled by the reverberation detection algorithm, a unique approach to reverberation mitigation was considered. This research project hypothesized that focusing mitigation on one effect of reverberation, either self-masking (masking within an individual phoneme) or overlap-masking (masking of one phoneme by a preceding phoneme) (Bolt and MacDonald, 1949), may allow for a reverberation mitigation strategy that operates in real-time. In order to determine the feasibility of this approach, the benefit of mitigating the two effects of reverberation was assessed by comparing speech recognition scores for speech in reverberation to reverberant speech after ideal self-masking mitigation and to reverberant speech after ideal overlap-masking mitigation. Testing was completed with normal hearing listeners via an acoustic model as well as with CI listeners using their devices. Mitigating either effect was found to improve CI speech recognition in reverberant environments. These results suggested that a new, causal approach could be taken to reverberation mitigation.</p><p>Based on the success of the feasibility study, two initial overlap-masking mitigation algorithms were implemented and applied once reverberation was detected in speech stimuli. One algorithm processed a pulse train signal after CI speech processing, while the second algorithm processed the acoustic signal. Performance of the two overlap-masking mitigation algorithms was evaluated in simulation by comparing pulses that were determined to be overlap-masking with the known truth. Using the features explored in this work, performance was comparable between the two methods. Therefore, only the post-CI speech processing reverberation mitigation algorithm was implemented in a CI speech processing strategy. </p><p>An initial experiment was conducted, using NH listeners and an acoustic model designed to present the frequency and temporal information that would be available to a CI listener. Listeners were presented with speech stimuli in the presence of both mitigated and unmitigated simulated reverberant conditions, and speech recognition was found to improve after reverberation mitigation. A subsequent experiment, also using NH listeners and an acoustic model, explored the effects of recorded room impulse responses (RIRs) and added noise (speech shaped noise and multi-talker babble) on the mitigation strategy. Because reverberation mitigation did not consistently improve speech recognition in these conditions, an analysis of the fundamental differences between simulated and recorded RIRs was conducted. Finally, CI listeners were presented with simulated reverberant speech, both with and without reverberation mitigation, and the effect of the mitigation strategy on speech recognition was studied. Because the reverberation mitigation strategy did not consistently improve speech recognition, future work is required to analyze the effects of algorithm-specific parameters for CI listeners.</p> / Dissertation

No description available.

Designing new microprocessors is a time consuming task. Architects rely on slow simulators to evaluate performance and a significant proportion of the design space has to be explored before an implementation is chosen. This process becomes more time consuming when compiler optimisations are also considered. Once the architecture is selected, a new compiler must be developed and tuned. What is needed are techniques that can speedup this whole process and develop a new optimising compiler automatically. This thesis proposes the use of machine-learning techniques to address architecture/compiler co-design. First, two performance models are developed and are used to efficiently search the design space of amicroarchitecture. These models accurately predict performance metrics such as cycles or energy, or a tradeoff of the two. The first model uses just 32 simulations to model the entire design space of new applications, an order of magnitude fewer than state-of-the-art techniques. The second model addresses offline training costs and predicts the average behaviour of a complete benchmark suite. Compared to state-of-the-art, it needs five times fewer training simulations when applied to the SPEC CPU 2000 and MiBench benchmark suites. Next, the impact of compiler optimisations on the design process is considered. This has the potential to change the shape of the design space and improve performance significantly. A new model is proposed that predicts the performance obtainable by an optimising compiler for any design point, without having to build the compiler. Compared to the state-of-the-art, this model achieves a significantly lower error rate. Finally, a new machine-learning optimising compiler is presented that predicts the best compiler optimisation setting for any new program on any new microarchitecture. It achieves an average speedup of 1.14x over the default best gcc optimisation level. This represents 61% of the maximum speedup available, using just one profile run of the application.