Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation

• URL: http://arxiv.org/abs/2403.07995v1
• Date: Tue, 12 Mar 2024 18:03:08 GMT
• Title: Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation
• Authors: Keshav Bhandari, Simon Colton
• Abstract summary: Modelling musical structure is vital yet challenging for artificial intelligence systems that generate symbolic music compositions. This literature review dissects the evolution of techniques for incorporating coherent structure. We outline several key future directions to realize the synergistic benefits of combining approaches from all eras examined.
• Score: 2.8062498505437055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modelling musical structure is vital yet challenging for artificial intelligence systems that generate symbolic music compositions. This literature review dissects the evolution of techniques for incorporating coherent structure, from symbolic approaches to foundational and transformative deep learning methods that harness the power of computation and data across a wide variety of training paradigms. In the later stages, we review an emerging technique which we refer to as "sub-task decomposition" that involves decomposing music generation into separate high-level structural planning and content creation stages. Such systems incorporate some form of musical knowledge or neuro-symbolic methods by extracting melodic skeletons or structural templates to guide the generation. Progress is evident in capturing motifs and repetitions across all three eras reviewed, yet modelling the nuanced development of themes across extended compositions in the style of human composers remains difficult. We outline several key future directions to realize the synergistic benefits of combining approaches from all eras examined.

Related papers

• 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)
• ComposerX: Multi-Agent Symbolic Music Composition with LLMs [51.68908082829048]
  Music composition is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints. Current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts. We propose ComposerX, an agent-based symbolic music generation framework.
  arXiv  Detail & Related papers  (2024-04-28T06:17:42Z)
• Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
  We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT) Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives. This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
  arXiv  Detail & Related papers  (2023-10-03T05:40:56Z)
• Motif-Centric Representation Learning for Symbolic Music [5.781931021964343]
  We learn the implicit relationship between motifs and their variations via representation learning. A regularization-based method, VICReg, is adopted for pretraining, while contrastive learning is used for fine-tuning. We visualize the acquired motif representations, offering an intuitive comprehension of the overall structure of a music piece.
  arXiv  Detail & Related papers  (2023-09-19T13:09:03Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data [77.34726150561087]
  This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. The model is able to produce fairly rich yet human-readable conceptual representations without training.
  arXiv  Detail & Related papers  (2023-07-16T15:59:13Z)
• WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning [26.515527387450636]
  WuYun is a knowledge-enhanced deep learning architecture for improving structure of generated melodies. We use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average.
  arXiv  Detail & Related papers  (2023-01-11T14:33:42Z)
• 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)
• Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music [69.2737664640826]
  This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically.
  arXiv  Detail & Related papers  (2020-12-02T14:19:19Z)
• Learning Interpretable Representation for Controllable Polyphonic Music Generation [5.01266258109807]
  We design a novel architecture, that effectively learns two interpretable latent factors of polyphonic music: chord and texture. We show that such chord-texture disentanglement provides a controllable generation pathway leading to a wide spectrum of applications.
  arXiv  Detail & Related papers  (2020-08-17T07:11:16Z)
• Modeling Musical Structure with Artificial Neural Networks [0.0]
  I explore the application of artificial neural networks to different aspects of musical structure modeling. I show how a connectionist model, the Gated Autoencoder (GAE), can be employed to learn transformations between musical fragments. I propose a special predictive training of the GAE, which yields a representation of polyphonic music as a sequence of intervals.
  arXiv  Detail & Related papers  (2020-01-06T18:35:57Z)

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.