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Humans are the best functioning example of multimedia communication and computing - that is, we understand information and experiences through the unified perspective offered by our five senses. This innovative textbook presents emerging techniques in multimedia computing from an experiential perspective in which each medium - audio, images, text, and so on - is a strong component of the complete, integrated exchange of information or experience. The authors' goal is to present current techniques in computing and communication that will lead to the development of a unified and holistic approach to computing using heterogeneous data sources. Gerald Friedland and Ramesh Jain introduce the fundamentals of multimedia computing, describing the properties of perceptually encoded information, presenting common algorithms and concepts for handling it, and outlining the typical requirements for emerging applications that use multifarious information sources. Designed for advanced undergraduate and beginning graduate courses, the book will also serve as an introduction for engineers and researchers interested in understanding the elements of multimedia and their role in building specific applications.
Besides current new trends related to “big data”, there is still room for a detailed analysis of the fine structure of small size signals. Typical examples are briefly presented, ranging from bioacoustics to mathematics, via physics. Illustrations motivate the usefulness of the methods to be introduced – that are to be described in the rest of the book, the idea being to come back to the analysis of those signals in the last chapter. All of those signals are chirps, with instantaneous characteristics (amplitude and frequency) and are captured by the notion of analytic signal. The ubiquity of chirps is supported by the listing of many additional examples in science and engineering.
Spectrograms are revisited from a Bargmann transform point of view, with the time-frequency plane identified to the complex plane. This permits to establish simple phase-magnitude relationships for the Gaussian STFT and to describe reassignment via a vector field which happens to be the gradient of the associated (log-)spectrogram. This also paves the way to variations such as differential or adjustable reassignment. Within this picture, the whole reassignment process can be described in terms of attractors (maxima), repellers (zeros), and basins of attraction (component domains).