S
Shyamala Doraisamy
Researcher at Universiti Putra Malaysia
Publications - 89
Citations - 1058
Shyamala Doraisamy is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: MIDI & Classifier (UML). The author has an hindex of 17, co-authored 85 publications receiving 916 citations. Previous affiliations of Shyamala Doraisamy include Information Technology University & Imperial College London.
Papers
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Journal ArticleDOI
Multi-level basis selection of wavelet packet decomposition tree for heart sound classification
Fatemeh Safara,Shyamala Doraisamy,Azreen Azman,Azrul Hazri Jantan,Asri Ranga Abdullah Ramaiah +4 more
TL;DR: Multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold.
Journal ArticleDOI
Robust Polyphonic Music Retrieval with N -grams
Shyamala Doraisamy,Stefan Rüger +1 more
TL;DR: This paper extends the n-gram approach for full-music indexing of monophonic music data to polyphonic music using both rhythm and pitch information and defines an experimental framework for a comparative and fault-tolerance study of various n- gramming strategies and encoding levels.
Proceedings Article
A study on feature selection and classification techniques for automatic genre classification of traditional Malay music
TL;DR: This study performs a more comprehensive investigation on improving the classification of Traditional Malay Music (TMM), identifying potentially useful classifiers and showing the impact of adding a feature selection phase for TMM genre classification.
Proceedings Article
Factors affecting automatic genre classification: an investigation incorporating non-western musical forms
TL;DR: Results show that various factors such as the musical features extracted, classifiers employed, the size of the dataset, excerpt length, excerpt location and test set parameters improve classification results.
Journal ArticleDOI
Wavelet Packet Entropy for Heart Murmurs Classification
TL;DR: New entropy was introduced to analyze heartSounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown, substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.