Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Analytical Features: A Knowledge-Based Approach to Audio Feature Generation | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2009 Article ID 153017 23 pages doi 2009 153017 Research Article Analytical Features A Knowledge-Based Approach to Audio Feature Generation Francois Pachet and Pierre Roy Sony CSL-Paris 6 rueAmyot 75005 Paris France Correspondence should be addressed to Francois Pachet pachet@ Received 4 September 2008 Accepted 16 January 2009 Recommended by Richard Heusdens We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features AFs a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs in particular their dimensional type system on which are based pattern-based random generators heuristics and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks demonstrating their advantage over standard audio features. More generally we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation. Copyright 2009 F. Pachet and P. Roy. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction This paper addresses two fundamental questions of human perception 1 to what extent are human perceptual categorization for items based on objective features of these items and 2 in these situations can we identify these objective features explicitly A natural paradigm for addressing these questions is supervised classification. Given a data set with perceptive labels considered as ground truth the question becomes how to train classifiers .
