The digitalization of communication creates novel answers to familiar questions – in the form of digital products. What must be done to define the genres of digital products? What must be done to enable the user to identify the genre? Such questions are not new. Art history refers to and to with the methods of stylistics and iconology, while the role of divides fine ...

We approach the general problem of classifying machine-printed documents into genres. Layout is a critical factor in recognizing fine-grained genres, as document content features are similar. Document genre is determined from the layout structure detected from scanned binary images of the document pages, using no OCR results and minimal a priori knowledge of document logical structures. Our met...

Modern search engines aim at classifying web pages not only according to topics, but also according to genres. This paper presents the results of an attempt to train a genre classifier. We present features extracted from a 20-genre corpus used for training the genre classifiers and the results of using different machine learning (ML) algorithms in the process of learning. Success of the genre c...

Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instr...

Genres of spoken and written texts are being intensively studied from various angles, e.g., communication studies, discourse analysis, computational linguistics, without arriving at a generally accepted definition. Many corpora have been built to represent the language, but very few large corpora indicate genres, and when they do the typology of genres varies widely. For instance, the Brown cor...

Constructing robust categorical and typological classifiers, i.e., finding auditory constructs utilized for describing music categories, is an important problem in music genre classification. Supervised methods such as support vector machine (SVM) achieve state of the art performance for genre classification but suffer from over-fitting on training examples. In this paper, we introduce a superv...

Recently a large amount of new chord annotations have been made available. This raises hopes for further development in automatic chord estimation. While more data seems to imply better performance, a major challenge however, is the wide variety of genres covered by these new data. As a result, the genre-independent training scheme as is common today is bound to fail. In this paper we investiga...

Automatic classification of music genre is widely studied topic in music information retrieval (MIR) as it is an efficient method to structure and organize the large numbers of music files available on the Internet. Generally, the genre classification process of music has two main steps: feature extraction and classification. The first step obtains audio signal information, while the second one...

Digital music distribution industry has seen a tremendous growth in resent years. Tasks such us automatic music genre discrimination address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we base on Self-Organizing Maps to map the high-dimension...

This paper examines automated genre classification of text documents and its role in enabling the effective management of digital documents by digital libraries and other repositories. Genre classification, which narrows down the possible structure of a document, is a valuable step in realising the general automatic extraction of semantic metadata essential to the efficient management and use o...