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Computational musicology can be generally divided into the three main branches relating to the three ways music can be represented by a computer: sheet music data, symbolic data, and audio data. Sheet music data refers to the human-readable, graphical representation of music via symbols. Examples of this branch of research would include ...
Sound and music computing (SMC) is a research field that studies the whole sound and music communication chain from a multidisciplinary point of view. By combining scientific, technological and artistic methodologies it aims at understanding, modeling and generating sound and music through computational approaches.
In many practical signal processing problems, the objective is to estimate from measurements a set of constant parameters upon which the received signals depend. There have been several approaches to such problems including the so-called maximum likelihood (ML) method of Capon (1969) and Burg's maximum entropy (ME) method.
This section only includes software, not services. For services programs like Spotify, Pandora, Prime Music, etc. see Comparison of on-demand streaming music services. Likewise, list includes music RSS apps, widgets and software, but for a list of actual feeds, see Comparison of feed aggregators.
One of the problems in modeling audio signals with factor oracle is the symbolization of features from continuous values to a discrete alphabet. This problem was solved in the Variable Markov Oracle (VMO) available as python implementation, [50] using an information rate criteria for finding the optimal or most informative representation. [51]
Mercury, a language for live-coding algorithmic music. Music Macro Language (MML), often used to produce chiptune music in Japan; MUSIC-N, includes versions I, II, III, IV, IV-B, IV-BF, V, 11, and 360; Nyquist; OpenMusic; Orca (music programming language) [1] Pure Data, a modular visual programming language for signal processing aimed at music ...
symbolic music modeling systems and computer-aided composition; social and economic realities of the consumption of music in Western societies; improvisation in music, especially where it is facilitated by music technology; music digital libraries and collections architectures; future of music distribution, the music industry, and music libraries
The software utilized music information processing and artificial intelligence techniques to essentially solve the transcription problem for simpler melodies, although higher-level melodies and musical complexities are regarded even today as difficult deep-learning tasks, and near-perfect transcription is still a subject of research. [6] [9]