Development of the Feature Extractor for Speech Recognition

Projecte final de carrera realitzat en col.laboració amb University of MariborWith this diploma work we have attempted to give continuity to the previous work done by other researchers called, Voice Operating Intelligent Wheelchair – VOIC [1]. A development of a wheelchair controlled by voice is presented in this work and is designed for physically disabled people, who cannot control their movements. This work describes basic components of speech recognition and wheelchair control system. Going to the grain, a speech recognizer system is comprised of two distinct blocks, a Feature Extractor and a Recognizer. The present work is targeted at the realization of an adequate Feature Extractor block which uses a standard LPC Cepstrum coder, which translates the incoming speech into a trajectory in the LPC Cepstrum feature space, followed by a Self Organizing Map, which classifies the outcome of the coder in order to produce optimal trajectory representations of words in reduced dimension feature spaces. Experimental results indicate that trajectories on such reduced dimension spaces can provide reliable representations of spoken words. The Recognizer block is left for future researchers. The main contributions of this work have been the research and approach of a new technology for development issues and the realization of applications like a voice recorder and player and a complete Feature Extractor system

Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition

The development of corpus-based computer assisted composition program and its application for instrumental music composition

In the last 20 years, we have seen the nourishing environment for the development of music software using a corpus of audio data expanding significantly, namely that synthesis techniques producing electronic sounds, and supportive tools for creative activities are the driving forces to the growth. Some software produces a sequence of sounds by means of synthesizing a chunk of source audio data retrieved from an audio database according to a rule. Since the matching of sources is processed according to their descriptive features extracted by FFT analysis, the quality of the result is significantly influenced by the outcomes of the Audio Analysis, Segmentation, and Decomposition. Also, the synthesis process often requires a considerable amount of sample data and this can become an obstacle to establish easy, inexpensive, and user-friendly applications on various kinds of devices. Therefore, it is crucial to consider how to treat the data and construct an efficient database for the synthesis. We aim to apply corpusbased synthesis techniques to develop a Computer Assisted Composition program, and to investigate the actual application of the program on ensemble pieces. The goal of this research is to apply the program to the instrumental music composition, refine its function, and search new avenues for innovative compositional method

Sensory information processing (1 January 1976 - 30 June 1976)

technical reportThe removal of the effects of atmospheric turbulence from optical images is a significant problem of long standing. Recent investigations by Knox and Thompson have led to the development of a restoration procedure which shows considerable promise. This procedure has not been successfully applied to real data as yet, however, nor has it been sufficiently well analyzed and simulated to provide a thorough quantitative understanding of their properties. Furthermore, these procedures will very likely require modification before they can be practically applied to large quantities of real data. We have begun an investigation of Knox's method aimed at finding suitable ways to apply it to real data

Cepstral Processing for GPS Multipath Detection and Mitigation

This work presents a novel approach to code phase multipath mitigation for Global Positioning System (GPS) receivers. It uses the power and complex cepstra for multipath detection and mitigation prior to code phase tracking by a standard non-coherent delay lock loop. Cepstral theory is presented to demonstrate how multipath reflection delays can be detected through the use of the power cepstrum. Filtering can then be performed on the complex cepstrum to remove multipath effects in the cepstral domain. Finally, an inverse complex cepstrum is calculated yielding a theoretically multipath free direct path estimate in the time domain. Simulations are presented to verify the applicability of cepstral techniques to the problem of GPS multipath mitigation. Results show that, under noiseless conditions, cepstral processing prior to code tracking by a standard non-coherent delay lock loop leads to lower code tracking biases than direct tracking of the composite multipath signal by a narrow correlator receiver. Finally, this work shows that cepstral processing is highly sensitive to additive white Gaussian noise effects, leading to the conclusion that methods of limiting noise effects must be developed before this technique will be applicable in actual GPS receivers