Music genre

Summary

A music genre is a conventional category that identifies some pieces of music as belonging to a shared tradition or set of conventions. It is to be distinguished from musical form and musical style, although in practice these terms are sometimes used interchangeably. Music can be divided into genres in varying ways, such as popular music and art music, or religious music and secular music. The artistic nature of music means that these classifications are often subjective and controversial, and some genres may overlap. In 2022, Douglass M. Green distinguishes between genre and form in his book Form in Tonal Music. He lists madrigal, motet, canzona, ricercar, and dance as examples of genres from the Renaissance period. To further clarify the meaning of genre, Green writes "Beethoven's Op. 61" and "Mendelssohn's Op. 64 ". He explains that both are identical in genre and are violin concertos that have different forms. However, Mozart's Rondo for Piano, K. 511, and the Agnus Dei from his Mass, K. 317, are quite different in genre but happen to be similar in form." In 1982, Franco Fabbri proposed a definition of musical genre that is now considered to be normative: "musical genre is a set of musical events (real or possible) whose course is governed by a definite set of socially accepted rules", where a musical event be defined as "any type of activity performed around any type of event involving sound". A music genre or subgenre may be defined by the musical techniques, the cultural context, and the content and spirit of the themes. Geographical origin is sometimes used to identify a music genre, though a single geographical category will often include a wide variety of subgenres. Timothy Laurie argues that, since the early 1980s, "genre has graduated from being a subset of popular music studies to being an almost ubiquitous framework for constituting and evaluating musical research objects". The term genre is generally defined similarly by many authors and musicologists, while the related term style has different interpretations and definitions.

Official source

https://en.wikipedia.org/wiki/Music_genre

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Related publications (1)

On the design of audio features robust to the album-effect for music information retrieval

Nicolas Scaringella

Short-term spectral features – and most notably Mel-Frequency Cepstral Coefficients (MFCCs) – are the most widely used descriptors of audio signals and are deployed in a majority of state-of-the-art Music Information Retrieval (MIR) systems. These descriptors have however demonstrated their limitations in the context of speech processing when training and testing conditions of the system do not match, like e.g. in noisy conditions or under a channel mismatch. A related problem has been observed in the context of music processing. It has indeed been hypothesized that MIR algorithms relying on the use of short-term spectral features were unexpectedly picking up on similarities in the production/mastering qualities of music albums. This problem has been referred to as the album-effect in the literature though it has never been studied in depth. It is showed in this thesis how the album-effect relates to the problem of channel mismatch. A measure of robustness to the album-effect is proposed and channel normalization techniques borrowed from the speech processing community are evaluated to help at improving the robustness of short-term spectral features. Alternatively, longer-term features describing critical-band specialized temporal patterns (TRAPs) are adapted to the context of music processing. It is shown how such features can help at describing either timbre or rhythm content depending on the scale considered for analysis and how robust they are to the album-effect. Contrarily to more classic short-term spectral descriptors, TRAP-based features encode some form of prior knowledge of the problem considered through a trained feature extraction chain. The lack of appropriately annotated datasets raises however some new issues when it comes to training the feature extraction chain. Advanced unsupervised learning strategies are considered in this thesis and evaluated against more traditional supervised approaches relying on coarse-grained annotations such as music genres. Specialized learning strategies and specialized architectures are also proposed to compensate for some inherent variability of the data due either to album-related factors or to the dependence of music signals to the tempo of the performance.

EPFL2009

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Related courses (20)

Related lectures (142)

On the design of audio features robust to the album-effect for music information retrieval

Nicolas Scaringella

EPFL2009