Models of Music Cognition and Composition
- URL: http://arxiv.org/abs/2208.06878v1
- Date: Sun, 14 Aug 2022 16:27:59 GMT
- Title: Models of Music Cognition and Composition
- Authors: Abhimanyu Sethia and Aayush
- Abstract summary: We first motivate why music is relevant to cognitive scientists and give an overview of the approaches to computational modelling of music cognition.
We then review literature on the various models of music perception, including non-computational models, computational non-cognitive models and computational cognitive models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Much like most of cognition research, music cognition is an interdisciplinary
field, which attempts to apply methods of cognitive science (neurological,
computational and experimental) to understand the perception and process of
composition of music. In this paper, we first motivate why music is relevant to
cognitive scientists and give an overview of the approaches to computational
modelling of music cognition. We then review literature on the various models
of music perception, including non-computational models, computational
non-cognitive models and computational cognitive models. Lastly, we review
literature on modelling the creative behaviour and on computer systems capable
of composing music. Since a lot of technical terms from music theory have been
used, we have appended a list of relevant terms and their definitions at the
end.
Related papers
- Do Music Generation Models Encode Music Theory? [10.987131058422742]
We introduce SynTheory, a synthetic MIDI and audio music theory dataset consisting of tempos, time signatures, notes, intervals, scales, chords, and chord progressions concepts.
We then propose a framework to probe for these music theory concepts in music foundation models and assess how strongly they encode these concepts within their internal representations.
Our findings suggest that music theory concepts are discernible within foundation models and that the degree to which they are detectable varies by model size and layer.
arXiv Detail & Related papers (2024-10-01T17:06:30Z) - A Survey of Foundation Models for Music Understanding [60.83532699497597]
This work is one of the early reviews of the intersection of AI techniques and music understanding.
We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
arXiv Detail & Related papers (2024-09-15T03:34:14Z) - Foundation Models for Music: A Survey [77.77088584651268]
Foundations models (FMs) have profoundly impacted diverse sectors, including music.
This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music.
arXiv Detail & Related papers (2024-08-26T15:13:14Z) - MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
We are inspired by how musicians compose music not just from a movie script, but also through visualizations.
We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music.
Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
arXiv Detail & Related papers (2024-06-07T06:38:59Z) - Music Composition with Deep Learning: A Review [1.7188280334580197]
We analyze the ability of current Deep Learning models to generate music with creativity.
We compare these models to the music composition process from a theoretical point of view.
arXiv Detail & Related papers (2021-08-27T13:53:53Z) - MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training [97.91071692716406]
Symbolic music understanding refers to the understanding of music from the symbolic data.
MusicBERT is a large-scale pre-trained model for music understanding.
arXiv Detail & Related papers (2021-06-10T10:13:05Z) - Music Embedding: A Tool for Incorporating Music Theory into
Computational Music Applications [0.3553493344868413]
It is important to digitally represent music in a music theoretic and concise manner.
Existing approaches for representing music are ineffective in terms of utilizing music theory.
arXiv Detail & Related papers (2021-04-24T04:32:45Z) - Music Harmony Generation, through Deep Learning and Using a
Multi-Objective Evolutionary Algorithm [0.0]
This paper introduces a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music.
One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners, fits the cycle of evolution to get the most optimal response.
The results show that the proposed method is able to generate difficult and pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar while attracting the listener, at the same time.
arXiv Detail & Related papers (2021-02-16T05:05:54Z) - Adaptive music: Automated music composition and distribution [0.0]
We present Melomics: an algorithmic composition method based on evolutionary search.
The system has exhibited a high creative power and versatility to produce music of different types.
It has also enabled the emergence of a set of completely novel applications.
arXiv Detail & Related papers (2020-07-25T09:38:06Z) - Artificial Musical Intelligence: A Survey [51.477064918121336]
Music has become an increasingly prevalent domain of machine learning and artificial intelligence research.
This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit.
arXiv Detail & Related papers (2020-06-17T04:46:32Z) - Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with
Visual Computing for Improved Music Video Analysis [91.3755431537592]
This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective.
The main hypothesis of this work is based on the observation that certain expressive categories such as genre or theme can be recognized on the basis of the visual content alone.
The experiments are conducted for three MIR tasks Artist Identification, Music Genre Classification and Cross-Genre Classification.
arXiv Detail & Related papers (2020-02-01T17:57:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.