%0 Journal Article %1 1592529 %A Pachet, Francois %A Roy, Pierre %C New York, NY, United States %D 2009 %I Hindawi Publishing Corp. %J EURASIP J. Audio Speech Music Process. %K analytical feature_aggregation feature_generation features genetic_programming mfcc %P 1--23 %R 10.1155/2009/153017 %T Analytical features: a knowledge-based approach to audio feature generation %U http://portal.acm.org/citation.cfm?id=1592529&dl=GUIDE&coll=GUIDE&CFID=87319572&CFTOKEN=29577946# %V 2009 %X 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.

@article{1592529, abstract = {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.}, added-at = {2010-05-06T10:39:13.000+0200}, address = {New York, NY, United States}, author = {Pachet, Fran\c{c}ois and Roy, Pierre}, biburl = {https://www.bibsonomy.org/bibtex/25e7bf7af20e8a626d0e655caec51ec18/andre@ismll}, description = {Analytical features}, doi = {10.1155/2009/153017}, interhash = {00308094eb08778be6788b68aab7fca3}, intrahash = {5e7bf7af20e8a626d0e655caec51ec18}, issn = {1687-4714}, journal = {EURASIP J. Audio Speech Music Process.}, keywords = {analytical feature_aggregation feature_generation features genetic_programming mfcc}, pages = {1--23}, publisher = {Hindawi Publishing Corp.}, timestamp = {2010-06-02T11:09:54.000+0200}, title = {Analytical features: a knowledge-based approach to audio feature generation}, url = {http://portal.acm.org/citation.cfm?id=1592529&dl=GUIDE&coll=GUIDE&CFID=87319572&CFTOKEN=29577946#}, volume = 2009, year = 2009 }