Non-stationary Signal Feature Extraction and Analysis

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Signal processing continues to play a fundamental role in many technological innovations and advancements related to speech, multimedia, healthcare, defense, security, telecommunications, Internet,  and energy systems. For the past 15 years, the Signal Analysis Research (SAR) Group at Ryerson University is involved in developing various innovative techniques and algorithms for processing and analysis of speech, audio, multimedia and biomedical signals. The underlying characteristics of signals involved with these systems is that they are complex, typically long duration, difficult to interpret, and have time-varying properties. In order to extract valuable information (features) from these signals and characterize events of interest, and to automatically classify patterns, sophisticated signal analysis algorithms (and analytical tools) need to be designed. The proposed NSERC Discovery Grant research will systematically investigate and develop mathematical methods, algorithms and tools to map 1-dimensional (1D) signals into higher dimensions for automatically extracting signal features at multiple levels, which are otherwise difficult or impossible to extract from conventional techniques. It is envisioned the mathematical transformation of signals to higher dimensions and the subsequent feature extraction algorithms will reveal underlying signal generation/modification mechanisms that could be useful in recognizing hidden/subtle signatures for better recognition and classification applications. The extracted signal features will be further coupled with appropriate machine learning algorithms in providing enhanced and robust recognition and classification performance efficiencies. Automatic feature extraction and analysis has lots of practical applications, and is the foundation of everyday systems encountered in speech, audio, multimedia,  biometrics and many other intelligent systems. The algorithms will be applied to real world datasets collected in our lab and other open source databases. The algorithms and the databases will also be shared with other interested research groups for the benefit of their specific domain of application (e.g., big data analytics in energy or health sector). The research program will also train a large number of highly qualified personnel who could eventually lead technological advancement in various industry and research sectors that are crucial for the societal well-being and economic prosperity of Canada.**