Music Genre Classification

La musica si offre al nostro orecchio e al nostro esserne gli interpreti bilanciando sempre quelle che sembrano essere le sue due qualità distintive e contrapposte: l’ineffabilità e la capacità di comunicare sensazioni, atmosfere, storie in maniera chirurgica. La faccenda si complica ulteriormente quando abbiamo a che fare con la musica che ascoltiamo tutti i giorni, attraverso quelli che ci ostiniamo a chiamare dischi. È la musica che chiamiamo registrata e che, però, quasi mai è registrata e basta. La musica rappresenta un problema per la semiotica, che come tale l’ha sempre trattata. Al punto che è forse questo il suo campo di applicazione più trascurato. Il significato della musica è stratificato: ha a che fare tanto con i suoni che essa contiene, e che ci sottopone in presa diretta o in forma mediata, quanto con le parole e le pratiche che costruiamo attorno a questi suoni, e che finiscono per orientarne profondamente il senso. Questo libro si concentra su alcune questioni centrali per una semiotica della musica che voglia comprenderla come fatto comunicativo, proponendo alcuni modelli per provare ad attraversare questo campo con uno sguardo — un orecchio — nuovo: la mediazione e il linguaggio fonografico; l’enunciazione e le sue diverse configurazioni; la nozione di genere e i generi musicali intesi come sistema discorsivo, dove la musica si dà appieno nella sua dimensione sociale e al cui interno tradizione e innovazione finiscono per sciogliersi l’una nell’altra.

We present a new approach for classifying mpeg-2 video sequences as 'cartoon' or 'non-cartoon' by analyzing specific video and audio features of consecutive frames in real-time. This is part of the well-known video-genre- classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 (12).

Este artículo propone una reformulación de las teorías sobre géneros musicales en músicas populares urbanas de acuerdo a la concepción de la genealogía que Michel Foucault establece en base a la obra de Friedrich Nietzsche. Para probar la pertinencia de estos acercamientos se aplicarán al desarrollo del post-rock desde su emergencia en los años 90 hasta la actualidad.

In this article a number of musical features are extracted from a large musical database and these were subsequently used to build four composer-classification models. The first two models, an if–then rule set and a decision tree, result in an understanding of stylistic differences between Bach, Haydn, and Beethoven. The other two models, a logistic regression model and a support vector machine classifier, are more accurate. The probability of a piece being composed by a certain composer given by the logistic regression model is integrated into the objective function of a previously developed variable neighborhood search algorithm that can generate counterpoint. The result is a system that can generate an endless stream of contrapuntal music with composer-specific characteristics that sounds pleasing to the ear. This system is implemented as an Android app called FuX.

This paper discusses the results of the MIREX 2005 symbolic genre classification contest and describes the Bodhidharma system, which attained the highest classification success rates in all four of the evaluated categories. Five systems were submitted to this contest, which was conducted independently at the University of Illinois at Urbana-Champagne (UIUC). Each system was evaluated in two different experiments, one

The concept of Raga and Tala is integral part of Indian Classical music. Raga is the melodic component while Tala is the rhythmic component in the music. Hence, Tala classification and identification is a paramount problem in the area of Music Information Retrieval (MIR) systems. Although there are seven basic Talas in Carnatic Music, a further subdivision of them gives a total of 175 ragas. Statistical and machine learning approaches are proposed in Literature Survey to classify Talas. However, they use complete musical recording for training and testing. As part of this paper, a novel approach is proposed for the first time in Carnatic music to classify Talas using repetitive structure called Thumbnails.

PUNK 3 (2) pp. 147–158 Intellect Limited 2014
Punk & Post-Punk Volume 3 Number 2
© 2014 Intellect Ltd Article. English language. doi: 10.1386/punk.3.2.147_1
Foetus-art-terrorism: Deciphering genre, intertextuality and noise in J. G. Thirlwell’s early musical corpus (1981–1988)
Ursula-Helen Kassaveti, Aristotle University of Thessaloniki

MA Thesis. Supervision: Jerrold Levinson (U. of Maryland) and Julien Deonna (U. of Geneva).
This essay is a conceptual analysis on the nature of musical genres. The main points are that they can be considered as objective social objects and that classifying a piece into a genre matters a lot for aesthetic evaluation.

More precisely I argue that musical genres are an underevaluated aspects of æsthetic evaluation mostly because they set conventionnally expected aesthetic expectations. They do that in an epistemologically objective way thanks to being what John Searle calls status functions. Furthermore, accurate categorizations of a musical piece into genres depend strongly on the piece's ontological features such as its musical (formal) structure, its musical medium (score, improvisation, recordings, ...), its historical context of creation, its process of transmission, and, importantly, the artists' categorial intentions.

The concept of Raga and Tala is integral part of Indian Classical music. Raga is the melodic component while Tala is the rhythmic component in the music. Hence, Tala classification and identification is a paramount problem in the area of Music Information Retrieval (MIR) systems. Although there are seven basic Talas in Carnatic Music, a further subdivision of them gives a total of 175 ragas. Statistical and machine learning approaches are proposed in Literature Survey to classify Talas. However, they use complete musical recording for training and testing. As part of this paper, a novel approach is proposed for the first time in Carnatic music to classify Talas using repetitive structure called Thumbnails.

Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there is no clear and formal definition of what genre is. Music categorizations are vague and unclear, suffering from human subjectivity and lack of agreement. In its first part, this paper offers a survey trying to cover the many different aspects of the matter. Its main goal is give the reader an overview of the history and the current state-of-the-art, exploring techniques and datasets used to the date, as well as identifying current challenges, such as this ambiguity of genre definitions or the introduction of human-centric approaches. The paper pays special attention to new trends in machine learning applied to the music annotation problem. Finally, we also include a music genre classification experiment that compares different machine learning models using Audioset.

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates.

While it is clear that the full emotional effect of a movie scene is carried through the successful interpretation of audio and visual information, music still carries a significant impact for interpretation of the director's intent and style. The intent of this study was to provide a preliminary understanding on a new database for the impact of timbral and select rhythm features in characterizing the differences among movie genres based on their film scores. For this study, a database of film scores from 98 movies was collected containing instrumental (non-vocal) music from 25 romance, 25 drama, 23 horror, and 25 action movies. Both pair-wise genre classification and classification with all four genres was performed using support vector machines (SVM) in a ten-fold cross-validation test. The results of the study support the notion that high intensity movies (i.e., Action and Horror) have musical cues that are measurably different from the musical scores for movies with more measured expressions of emotion (i.e., Drama and Romance).

El estudio de la dimensión temporal de los géneros musicales es uno de los aspectos menos trabajados de los estudios sobre músicas populares urbanas. Más allá de puntuales referencias al carácter dinámico de los géneros musicales, pocos han sido los autores que han abordado de manera sistemática cómo se transforman estos a lo largo de su historia. En este artículo se realizará una revisión crítica de los principales planteamientos realizados al respecto, formulados por Franco Fabbri, Fabian Holt, Jennifer C. Lena y David Brackett. Más allá del resumen de sus teorías, el propósito de este trabajo es reflexionar sobre los aspectos en común, las lagunas y las disensiones que nos plantean sus propuestas de cara a abordar esta problemática. Esto nos llevará a revisar algunos conceptos relevantes para abordar históricamente los géneros musicales, prestando especial atención a las ideas de convención y comunidad.

This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted from each music segment. At the classification, the outputs provided by each individual classifier are combined through simple combination rules such as majority vote, max, sum and product rules, with the aim of improving music genre classification accuracy. Experiments carried out on a large dataset containing more than 3,000 music samples from ten different Latin music genres have shown that for the task of automatic music genre classification, the features extracted from the middle part of the music provide better results than using the segments from the beginning or end part of the music. Furthermore, the proposed ensemble approach, which combines the multiple feature vectors, provides better accuracy than using single classifiers and any individual music segment.

This chapter explores how Italian music magazines covered a music genre - the folk revival - which rose to prominence during the mid-1970s. It explores magazines' competing coverage strategies and critics' struggle over the socio-political meaning of Italian folk acts. The chapter contributes to genre theory unravelling the contested nature of music genres and critics' competing readings of their properties and values.

We present a new approach for classifying mpeg-2 video sequences as 'cartoon' or 'non-cartoon' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video- genre-classification problem, where popular TV- broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of

The indie classical genre is generally used to describe the music that follows certain classical music practices but is produced and distributed through independent record labels. The term was brought into widespread circulation by New Amsterdam Records’ publicity apparatus, intended to represent composers whose “music slips through the cracks between genres.”