Music genreA 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.
Document classificationDocument classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science.
Pop musicPop music is a genre of popular music that originated in its modern form during the mid-1950s in the United States and the United Kingdom. During the 1950s and 1960s, pop music encompassed rock and roll and the youth-oriented styles it influenced. Rock and pop music remained roughly synonymous until the late 1960s, after which pop became associated with music that was more commercial, ephemeral, and accessible.
Dance musicDance music is music composed specifically to facilitate or accompany dancing. It can be either a whole piece or part of a larger musical arrangement. In terms of performance, the major categories are live dance music and recorded dance music. While there exist attestations of the combination of dance and music in ancient times (for example Ancient Greek vases sometimes show dancers accompanied by musicians), the earliest Western dance music that we can still reproduce with a degree of certainty are old-fashioned dances.
Soul musicSoul music is a popular music genre that originated in the African American community throughout the United States in the late 1950s and early 1960s. It has its roots in African-American gospel music and rhythm and blues. Soul music became popular for dancing and listening, where U.S. record labels such as Motown, Atlantic and Stax were influential during the Civil Rights Movement. Soul also became popular around the world, directly influencing rock music and the music of Africa.
GenreGenre (UK: /ˈʒɑ̃ː.rə/, /ˈʒɒn.rə/; US: /ˈʒɑːn.rə/) () is any form or type of communication in any mode (written, spoken, digital, artistic, etc.) with socially-agreed-upon conventions developed over time. In popular usage, it normally describes a of literature, music, or other forms of art or entertainment, whether written or spoken, audio or visual, based on some set of stylistic criteria. Genres can be aesthetic, rhetorical, communicative, or functional.
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.
Experimental musicExperimental music is a general label for any music or music genre that pushes existing boundaries and genre definitions. Experimental compositional practice is defined broadly by exploratory sensibilities radically opposed to, and questioning of, institutionalized compositional, performing, and aesthetic conventions in music. Elements of experimental music include indeterminacy, in which the composer introduces the elements of chance or unpredictability with regard to either the composition or its performance.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
