Shyamala Doraisamy

Bio: 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.

Robust Polyphonic Music Retrieval with N -grams

Abstract: In this paper we investigate the retrieval performance of monophonic and polyphonic queries made on a polyphonic music database. We extend the n-gram approach for full-music indexing of monophonic music data to polyphonic music using both rhythm and pitch information. We define an experimental framework for a comparative and fault-tolerance study of various n-gramming strategies and encoding levels. For monophonic queries, we focus in particular on query-by-humming systems, and for polyphonic queries on query-by-example. Error models addressed in several studies are surveyed for the fault-tolerance study. Our experiments show that different n-gramming strategies and encoding precision differ widely in their effectiveness. We present the results of our study on a collection of 6366 polyphonic MIDI-encoded music pieces.

A study on feature selection and classification techniques for automatic genre classification of traditional Malay music

Abstract: Machine learning techniques for automated musical genre classification is currently widely studied. With large collections of digital musical files, one approach to classification is to classify by musical genres such as pop, rock and classical in Western music. Beat, pitch and temporal related features are extracted from audio signals and various machine learning algorithms are applied for classification. Features that resulted in better classification accuracies for Traditional Malay Music (TMM), in comparison to western music, in a previous study were beat related features. However, only the J48 classifier was used and in this study we perform a more comprehensive investigation on improving the classification of TMM. In addition, feature selection was performed for dimensionality reduction. Classification accuracies using classifiers of varying paradigms on a dataset comprising ten TMM genres were obtained. Results identify potentially useful classifiers and show the impact of adding a feature selection phase for TMM genre classification.

Factors affecting automatic genre classification: an investigation incorporating non-western musical forms

Abstract: The number of studies investigating automated genre classification is growing following the increasing amounts of digital audio data available. The underlying techniques to perform automated genre classification in general include feature extraction and classification. In this study, MARSYAS was used to extract audio features and the suite of tools available in WEKA was used for the classification. This study investigates the factors affecting automated genre classification. As for the dataset, most studies in this area work with western genres and traditional Malay music is incorporated in this study. Eight genres were introduced; Dikir Barat, Etnik Sabah, Inang, Joget, Keroncong, Tumbuk Kalang, Wayang Kulit, and Zapin. A total of 417 tracks from various Audio Compact Discs were collected and used as the dataset. 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.

Wavelet Packet Entropy for Heart Murmurs Classification

Abstract: Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.

Classification in the Presence of Label Noise: A Survey

Abstract: Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

Information retrieval

Abstract: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Density-based clustering based on hierarchical density estimates

Abstract: We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.