This paper discusses a new approach for clustering musical bass-line patterns representing particular genres and its application to audio genre classification. Many musical genres are characterized not only by timbral information but also by distinct representative bass-line patterns. So far this kind of temporal features have not so effectively been utilized. In particular, modern music songs ...

In this paper, we report on a preliminary study we carried out for identifying patterns that characterize the genre type of Greek texts. In the course of our study, we address four distinct genre types, we record their observable stylistic elements and we indicate their exploitation for automatic genre-based document classification. The findings of our study demonstrate that texts contain lexic...

This paper presents an automatic genre classification model that implements a flexible classification scheme, i.e. a scheme capable of performing zero-, oneor multi-genre assignment. I suggest that this scheme is more appropriate for genres on the web, because many web pages have often more than one genre or none at all. The model that I propose relies on the distinction between the concepts of...

Genre definition and attribution is generally considered to be subjective. This makes evaluation of any genrelabelling system intrinsically difficult, as the ground-truth against which it is compared is based upon subjective responses, with little inter-participant consensus. This paper presents a novel method of analysing the results of a genre-labelling task, and demonstrates that there are g...

In this paper two sets of evaluation experiments are conducted. First, we compare state-of-the-art automatic music genre classification algorithms to human performance on the same dataset, via a listening experiment. This will show that the improvements of contentbased systems over the last years have reduced the gap between automatic and human classification performance, but could not yet clos...

In this paper, music genre classification is addressed in a multilinear perspective. Inspired by a model of auditory cortical processing, multiscale spectro-temporal modulation features are extracted. Such spectro-temporal modulation features have been successfully used in various content-based audio classification tasks recently, but not yet in music genre classification. Each recording is rep...

Recently, with the construction of digital music libraries, it is important to efficiently manage a large music database. It will be helpful to provide a content-based music genre classification system for managing a large database. Therefore, in this paper we will propose two novel music features, low-frequency energy ratio (LFER) and energy domain signal coding (EDSC), for music genre classif...

In the ESP tradition, genre is often defined as “structured communicative events engaged in by specific discourse communities whose members share broad communicative purposes.” ESP genre research has generated numerous descriptions and explanations of discipline-specific genre exemplars and has produced various pedagogical proposals. However, what learners learn from these genre descriptions an...

We argue for a systematic analysis of one particular, well structured domain—academic Web pages—with regard to a special class of digital genres: Web genres. For this purpose, we have developed a database-driven system that will ultimately consist of more than 3 000 000 HTML documents, written in German, which are the empirical basis for our research. We introduce the notions of Web genre type ...

Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled dat...