Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Instance-Based Learning Algorithms
Machine Learning
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
C4.5: programs for machine learning
C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Design of Hierarchical Classifiers
Proceedings of the The First Great Lakes Computer Science Conference on Computing in the 90's
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Musical instrument recognition using cepstral coefficients and temporal features
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
EURASIP Journal on Applied Signal Processing
MIRAI: Multi-hierarchical, FS-Tree Based Music Information Retrieval System
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Discriminant feature analysis for music timbre recognition and automatic indexing
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Instrument recognition in polyphonic music based on automatic taxonomies
IEEE Transactions on Audio, Speech, and Language Processing
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Recently, numerous successful approaches have been developed for instrument recognition in monophonic sounds. Unfortunately, none of them can be successfully applied to polyphonic sounds. Identification of music instruments in polyphonic sounds is still difficult and challenging. This has stimulated a number of research projects on music sound separation, new features development, and more recently on hierarchically structured classifiers used in content-based music recommender systems. This paper introduces a hierarchically structured cascade classification system to estimate multiple timbre information from the polyphonic sound by classification which is based on acoustic features and short-term power spectrum matching. This cascade system makes a first estimate on the higher level decision attribute which stands for the musical instrument family. Then, the further estimation is done within that specific family range. Experiments showed better performance of a hierarchical system than the traditional flat classification method which directly estimates the instrument without higher level of family information analysis. Traditional hierarchical structures were constructed in human semantics, which are meaningful from human perspective but not appropriate for a cascade system. We introduce a new hierarchical instrument schema according to the clustering results of the acoustic features. This new schema better describes the similarity among different instruments or among different playing techniques of the same instrument. The classification results show the higher accuracy of cascade system with the new schema compared to the traditional schemas.