Musical Sound Recognition by Active Learning PNN
Identifieur interne : 000938 ( Main/Exploration ); précédent : 000937; suivant : 000939Musical Sound Recognition by Active Learning PNN
Auteurs : Bülent Bolat [Turquie] ; Ünal Küçük [Turquie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
English descriptors
- Teeft :
- Active learner, Algorithm, Basic architecture, Best accuracy, Best analysis orders, Better feature sets, Better results, Better training, Bolat, Bottom level, Cent training test training test feat, Classifier, Decision rule, Different orders, Entire sample space, Eronen, Exchange process, Feature sets, First step, First task, Generalization performance, Ieee trans, Individual instrument recognition rates, Individual instrument recognition task, Input layer, Instrument recognition, Learner, Mfcc, Mfcc order, Musical instrument recognition, Musical sound recognition, Neural, Neural information processing systems, Neural networks, Neuron, Order mfcc, Original location, Output layer, Passive observer, Passive pnns, Pattern layer neurons, Pattern vector, Probabilistic, Probability density functions, Proc, Recent works, Redundant instances, Second level classifiers, Summation layer, Test accuracies, Test accuracy, Test data, Training data, Training pattern, Training patterns, Training sets, Training test, Windowed waveform.
Abstract
Abstract: In this work an active learning PNN was used to recognize instru-mental sounds. LPC and MFCC coefficients with different orders were used as features. The best analysis orders were found by using passive PNNs and these sets were used with active learning PNNs. By realizing some experiments, it was shown that the entire performance was improved by using the active learning algorithm.
Url:
DOI: 10.1007/11848035_63
Affiliations:
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Le document en format XML
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LPC and MFCC coefficients with different orders were used as features. The best analysis orders were found by using passive PNNs and these sets were used with active learning PNNs. By realizing some experiments, it was shown that the entire performance was improved by using the active learning algorithm.</div></front></TEI><affiliations><list><country><li>Turquie</li></country></list><tree><country name="Turquie"><noRegion><name sortKey="Bolat, Bulent" sort="Bolat, Bulent" uniqKey="Bolat B" first="Bülent" last="Bolat">Bülent Bolat</name></noRegion><name sortKey="Bolat, Bulent" sort="Bolat, Bulent" uniqKey="Bolat B" first="Bülent" last="Bolat">Bülent Bolat</name><name sortKey="Kucuk, Unal" sort="Kucuk, Unal" uniqKey="Kucuk U" first="Ünal" last="Küçük">Ünal Küçük</name><name sortKey="Kucuk, Unal" sort="Kucuk, Unal" uniqKey="Kucuk U" first="Ünal" last="Küçük">Ünal Küçük</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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