Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence Review - Special issue on lazy learning
KDD-Based Approach to Musical Instrument Sound Recognition
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Application of Temporal Descriptors to Musical Instrument Sound Recognition
Journal of Intelligent Information Systems
Blind Separation of Multiple Speakers in a Multipath Environment
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Speech recognition with dynamic bayesian networks
Speech recognition with dynamic bayesian networks
Estimation of musical sound separation algorithm effectiveness employing neural networks
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
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
Knowledge discovery-based identification of musical pitches and instruments in polyphonic sounds
Engineering Applications of Artificial Intelligence
Musical instrument timbres classification with spectral features
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
Quality of musical instrument sound identification for various levels of accompanying sounds
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
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In our continuing work on ”Blind Signal Separation” this paper focuses on extending our previous work [1] by creating a data set that can successfully perform blind separation of polyphonic signals containing similar instruments playing similar notes in a noisy environment. Upon isolating and subtracting the dominant signal from a base signal containing varying types and amounts of noise, even though we purposefully excluded any identical matches in the dataset, the signal separation system successfully built a resulting foreign set of synthesized sounds that the classifier correctly recognized. Herein, this paper presents a system that classifies and separates two harmonic signals with added noise. This novel methodology incorporates Knowledge Discovery, MPEG7-based segmentation and Inverse Fourier Transforms.