Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features. To overcome this, the combination of multiple different feature sets bearing diverse characteristics is needed. We propose a new approach to extend the scope of the features: We transcribe audio data into a symbolic form using a transcription system, extract symbolic descriptors from that representation and combine them with audio features. With this method, we are able to surpass the glass ceiling and to further improve music genre classification, as shown in the experiments through three reference music databases and comparison to previously published performance results. 1

There is an increasing interest in music information retrieval for reference, motive, or thumbnail extraction from a piece in order to have a compact and representative representation of the information to be retrieved. One of the main references for music is its melody. In a practical environment of symbolic format collections the information can be found in standard MIDI file format, structured as a number of tracks, usually one of them containing the melodic line, while the others contain the accompaniment. The goal of this work is to analyse how statistical rules can be used to characterize a melody in such a way that one can understand the solution of an automatic system for selecting the track containing the melody in such files. 1

Automatic music classification is a wide-ranging and multidisciplinary area of inquiry that offers significant benefits from both academic and commercial perspectives. This dissertation focuses on the development of jMIR, a suite of powerful, flexible, accessible and original software tools that can be used to design, share and apply a wide range of automatic music classification technologies. jMIR permits users to extract meaningful information from audio recordings, symbolic musical representations and cultural information available on the Internet; to use machine learning technologies to automatically build classification models; to automatically collect profiling statistics and detect metadata errors in musical collections; to perform experiments on large, stylistically diverse and well-labelled collections of music in both audio and symbolic formats; and to store and distribute information that is essential to automatic music classification in expressive and flexible standardised file formats. In order to have as diverse a range of applications as possible, care was taken to avoid tying jMIR to any particular types of music classification. Rather, it is designed to be a

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Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features. To overcome this, the combination of multiple different feature sets bearing diverse characteristics is needed. We propose a new approach to extend the scope of the features: We transcribe audio data into a symbolic form using a transcription system, extract symbolic descriptors from that representation and combine them with audio features. With this method, we are able to surpass the glass ceiling and to further improve music genre classification. In this work, the methodology of the system presented in [3] is described and evaluated. 1

Abstract — We present preliminary work on automatic human-readable melody characterization. In order to obtain such a characterization, we (1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files, (2) apply a rule induction algorithm to obtain a set of (crisp) classification rules for melody track identification, and (3) automatically transform the crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership functions for the rule attributes. Some results are presented and discussed. I.

There is an increasing interest towards music stored in digital format, which is witnessed by the widespread diffusion of standards for audio like MP3 and of web-based services to listen or purchase music. There is a number of reasons to explain such a diffusion of digital music. First of all, music crosses the barriers of national languages and cultural backgrounds and can be shared by people with different culture. Moreover, music is an art form that can be both cultivated and popular, and sometimes it is impossible to draw a line between the two, for instance in the case of jazz or of ethnic music. These reasons, among others, may explain the increasing number of projects involving the creation of music digital libraries. A music Digital Library (DL) allows for, and benefits from, the access by users from all over the world, it helps the preservation of cultural heritage, and it is not tailored only to scholars ' or researchers ' needs. The availability of music collections to a wide number of users, needs to be paired by the development of novel methodologies for accessing, retrieving, organizing, browsing, and recommending music. The research area devoted to this aspect is usually called Music Information Retrieval (MIR) although retrieval is only one of the relevant aspects. Given the particular nature of music language, which does not aim at describing objects or concepts, typical metadata

There is an increasing interest towards music stored in digital format, which is witnessed by the widespread diffusion of standards for audio like MP3 and of web-based services to listen or purchase music. There is a number of reasons to explain such a diffusion of digital music. First of all, music crosses the barriers of national languages and cultural backgrounds and can be shared by people with different culture. Moreover, music is an art form that can be both cultivated and popular, and sometimes it is impossible to draw a line between the two, for instance in the case of jazz or of ethnic music. These reasons, among others, may explain the increasing number of projects involving the creation of music digital libraries. A music Digital Library (DL) allows for, and benefits from, the access by users from all over the world, it helps the preservation of cultural heritage, and it is not tailored only to scholars ' or researchers ' needs. The availability of music collections to a wide number of users, needs to be paired by the development of novel methodologies for accessing, retrieving, organizing, browsing, and recommending music. The research area devoted to this aspect is usually called Music Information Retrieval (MIR) although retrieval is only one of the relevant aspects. Given the particular nature of music language, which does not aim at describing objects or concepts, typical metadata

Abstract. This paper provides an overview on the advances of music information retrieval in symbolic representation of music. Such musical aspects as key, tonality, bass, melody, dynamics, rhythm and patterns are considered as foundation for visualizing systems for the piano stream in classical music. The paper then describes the proposed visualizing system and Malinowski’s music animation machine. It lays light on the challenges facing creating contemporary visualizing systems. It is supplied with a related references list for further study on the issue. Keywords: Music content analysis, Visualization, MIDI, piano stream, MIR. 1

In this paper, we explore the classification of emotions in songs, using the music and the lyrics representation of the songs. We introduce a novel corpus of music and lyrics, consisting of 100 songs annotated for emotions. We show that textual and musical features can both be successfully used for emotion recognition in songs. Moreover, through comparative experiments, we show that the joint use of lyrics and music brings significant improvements over each of the individual textual and musical classifiers, with error rate reductions of up to 31%. 1