Abstract
Many music works produced in the last century still exist only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a digital format easy-to-manage which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. The manual process to carry out this task is very time consuming and error prone. Automatic optical music recognition (OMR) has emerged as a partial solution to this problem. However, the full potential of this process only reveals itself when integrated in a system that provides seamless access to browsing, retrieval, search and analysis. We address this demand by proposing a modular, flexible and scalable framework that fully integrates the abovementioned functionalities. A web based system to carry out the automatic recognition process, allowing the creation and management of a music corpus, while providing generalized access to it, is a unique and innovative approach to the problem. A prototype has been implemented and is being used as a test platform for OMR algorithms.

Abstract
This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian Information Criterion (BIC) are tested. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic thresholding followed by a fusion scheme, and finally applies BIC. The second method is a real-time one that uses a metric-based approach employing the line spectral pairs and the BIC to validate a potential speaker change point. The third method consists of three modules. In the first module, a measure based on second-order statistics is used; in the second module, the Euclidean distance and T-2 Hotelling statistic are applied; and in the third module, the BIC is utilized. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. A comparison between the performance of the three systems is made based on t-statistics.

Abstract
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a new rationale to include the information about the order in the design of a classification model. The method encompasses the inclusion of consistency constraints between adjacent decision regions. A new decision tree and a new nearest neighbour algorithms are then designed under that rationale. An experimental study with artificial and real data sets verifies the usefulness of the proposed approach. © 2010 IEEE.

Abstract
Many music works produced in the past are currently available only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a machine-readable format, which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. Carrying this task manually is very time consuming and error prone. While optical music recognition (OMR) systems usually perform well on printed scores, the processing of handwritten music by computers remains below the expectations. One of the fundamental stages to carry out this task is the detection and subsequent removal of staff lines. In this paper we integrate a general-purpose, knowledge-free method for the automatic detection of staff lines based on stable paths, into a recently developed staff line removal toolkit. Lines affected by curvature, discontinuities, and inclination are robustly detected. We have also developed a staff removal algorithm adapting an existing line removal approach to use the stable path algorithm at the detection stage, Experimental results show that the proposed technique outperforms well-established algorithms. The developed algorithm will now be integrated in a web based system providing seamless access to browsing, retrieval, search and analysis of submitted scores.

Abstract
Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation. Moreover, most of the techniques presented so far in the literature can generate ambiguous regions. All-at-Once methods have been proposed to solve this issue. In this work we devise a new SVM methodology based on the unimodal paradigm with the All-at-Once scheme for the ordinal classification. © 2010 IEEE.

Abstract
In this work we consider the problem of binary classification where the classifier may abstain instead of classifying each observation, leaving the critical items for human evaluation. This article motivates and presents a novel method to learn the reject region on complex data. Observations are replicated and then a single binary classifier determines the decision plane. The proposed method is an extension of a method available in the literature for the classification of ordinal data. Our method is compared with standard techniques on synthetic and real datasets, emphasizing the advantages of the proposed approach.