Adaptive signal analysis: novel methods and applications

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Signal processing has significantly advanced technologies in consumer electronics, medical devices, geophysical applications, financial analysis and many more. The proposed research will develop novel advanced signal processing methods adapted to real world signals typically encountered in biometrics and multimedia applications. The underlying characteristics of signals involved with these systems is that they are associated with time-varying behavior, they have time localized information, and also exhibit lot of variability with respect to the signal spectral properties. In order to extract information from the signal and characterize the event, and to understand the underlying environment, sophisticated signal analysis algorithms (and analytical tools) need to be designed. The proposed research will investigate emerging signal processing methodology that uses signal decomposition framework and representation for robust extraction of salient signal features. Feature extraction and analysis has lots of practical applications, and is the core of signal processing systems encountered in multimedia, biomedical, biometrics and many other intelligent systems. In the proposed research, the extracted features will be used for intelligently embedding information (watermarking and fingerprinting) in multimedia (audio) files for copyright protection, broadcast monitoring, and content identification. We will also investigate ubiquitous biometric systems that uses keystrokes and speech to identify the users. The key to successful design of these systems for human-machine interaction will also depend on the robust feature extraction module which is the main topic of investigation in this research. The algorithms will be applied to real world datasets collected in our lab and other open source databases. It is envisioned the feature extraction algorithms will reveal underlying signal generation/modification mechanisms that could be useful in recognizing hidden signatures for better recognition and classification applications in other areas of intelligent systems. The algorithms and the databases will also be shared with other interested research groups around the world.