Suppression of acoustic noise in speech using spectral subtraction

technical reportA stand alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital waveform. Spectral subtraction offers a computationally efficient, processor independent, approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise for speech by subtracting the spectral noise bias calculated during non-speech activity. Secondary procedures and then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a preprocessor to narrow band voice communications systems, speech recognition systems or speaker authentication systems

Audiovisual processing for sports-video summarisation technology

In this thesis a novel audiovisual feature-based scheme is proposed for the automatic summarization of sports-video content The scope of operability of the scheme is designed to encompass the wide variety o f sports genres that come under the description ‘field-sports’. Given the assumption that, in terms of conveying the narrative of a field-sports-video, score-update events constitute the most significant moments, it is proposed that their detection should thus yield a favourable summarisation solution. To this end, a generic methodology is proposed for the automatic identification of score-update events in field-sports-video content. The scheme is based on the development of robust extractors for a set of critical features, which are shown to reliably indicate their locations. The evidence gathered by the feature extractors is combined and analysed using a Support Vector Machine (SVM), which performs the event detection process. An SVM is chosen on the basis that its underlying technology represents an implementation of the latest generation of machine learning algorithms, based on the recent advances in statistical learning. Effectively, an SVM offers a solution to optimising the classification performance of a decision hypothesis, inferred from a given set of training data. Via a learning phase that utilizes a 90-hour field-sports-video trainmg-corpus, the SVM infers a score-update event model by observing patterns in the extracted feature evidence. Using a similar but distinct 90-hour evaluation corpus, the effectiveness of this model is then tested genencally across multiple genres of fieldsports- video including soccer, rugby, field hockey, hurling, and Gaelic football. The results suggest that in terms o f the summarization task, both high event retrieval and content rejection statistics are achievable

Electric power demand forecasting using wavelets and artificial neural networks

Various methods have been developed to handle electric power demand forecasting. Regression and periodic analysis are used to look over the long range and spot trends and cyclic behavior in power demand. These methods are concerned with structure and pattern that develops over relatively long periods of time. However, this type of analysis is ill suited for identifying and reacting to short-term trends and structure. Artificial Neural Networks can be used to draw correlations between current and past weather conditions and power demand. While this approach is useful for learning the conditions that contribute to power demand, the ability to detect long-term patterns is not as strong as with other methods. Wavelets Neural Nets offer a compromise in the forecast of electric power demand. With their time-frequency flexibility, wavelets are capable of identifying long- term structure, but also flexible enough to respond to short-term fluctuations. Since wavelets represent an alternative way of representing the data to be analyzed, it gives an Artificial Neural Network a time-frequency dimension with which to correlate and model raw data. In short, Wavelet Neural Networks bridge the gap between near-sighted Artificial Neural Nets and far-sighted Trend/Periodic analysis of electric power demand. This thesis investigates the design and implementation of a Wavelet Neural Network for electric power demand forecasting. Several different wavelet basis functions are used to gain insight into how the Neural Net can use the time-frequency information contained in the wavelet coefficients. The Wavelet Neural Net is also tested in conjunction with known inputs to ascertain the effect of the wavelet coefficients on predictive capability for a known problem

Reduction of impacts of oil and gas operations through intelligent maintenance solution

Impacts of oil and gas production operations are always very obvious when there is imbalanced operation, uncontrolled stoppage or catastrophic failure of the system during normal operations. These impacts may range from high flaring and venting of associated petroleum gas, oil release or spillage, equipment damage, fire outbreak to even fatality. Possible causes of imbalanced operations or system failure are categorised into process upset, system degradation, ineffective operation and maintenance procedures and human errors. Effective maintenance strategy integrates major components of the system; people (human factors), operation and maintenance procedures (process) and production plant (technology) to develop an intelligent maintenance solution that is capable of monitoring and detecting fault in the system at incipient stage before operational integrity is compromised. This paper deploys data-based analytics technique to develop condition-based predictive maintenance system to monitor, predict and classify performance of gas processing system. Exhaust gas temperature (EGT) of Gas Turbine Engine (GTE) is one of the operating and control parameters associated with efficiency of the GTE operation. The EGT is measured using several thermocouples, temperature sensors spaced equidistant around the circumference of the exhaust duct of the GTE. Neural network technique of multisensory data fusion is integrated with intelligent maintenance system to monitor performance of GTE, detect fault and classify performance of GTE to optimal, average and abnormal performance

Hidden Markov models and neural networks for speech recognition

The Hidden Markov Model (HMMs) is one of the most successful modeling approaches for acoustic events in speech recognition, and more recently it has proven useful for several problems in biological sequence analysis. Although the HMM is good at capturing the temporal nature of processes such as speech, it has a very limited capacity for recognizing complex patterns involving more than first order dependencies in the observed data sequences. This is due to the first order state process and the assumption of state conditional independence between observations. Artificial Neural Networks (NNs) are almost the opposite: they cannot model dynamic, temporally extended phenomena very well, but are good at static classification and regression tasks. Combining the two frameworks in a sensible way can therefore lead to a more powerful model with better classification abilities. The overall aim of this work has been to develop a probabilistic hybrid of hidden Markov models and neural networks and ..