Various Approaches to Reasoning With Frequency Based Decision Reducts: A Survey

Different aspects of reduct approximations are discussed. In particular, we show how to use them to develop flexible tools for analysis of strongly inconsistent and/or noisy data tables. A special attention is paid to the notion of a rough membership decision reduct -- a feature subset (almost) preserving the frequency based information about conditions!decision dependencies. Approximate criteria of preserving such a kind of information under attribute reduction are considered. These criteria are specified by using distances between frequency distributions and information measures related to different ways of interpreting rough membership based knowledge

Problems with Automatic Classification of Musical Sounds

Convenient searching of multimedia databases requires well annotated data. Labeling sound data with information like pitch or timbre must be done through sound analysis. In this paper, we deal with the problem of automatic classification of musical instrument on the basis of its sound. Although there are algorithms for basic sound descriptors extraction, correct identification of instrument still poses a problem. We describe di#culties encountered when classifying woodwinds, brass, and strings of contemporary orchestra. We discuss most di#cult cases and explain why these sounds cause problems. The conclusions are drawn and presented in brief summary closing the paper