An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores

• URL: http://arxiv.org/abs/2301.05908v1
• Date: Sat, 14 Jan 2023 12:30:16 GMT
• Title: An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores
• Authors: Xin Jin, Wu Zhou, Jinyu Wang, Duo Xu, Yiqing Rong, Shuai Cui
• Abstract summary: The quality of music score generated by AI is relatively poor compared with that created by human composers. This paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment.
• Score: 8.751312368054016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emotion. Based on Birkhoff's aesthetic measure, this paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment. The main contributions of our work are as follows: first, we put forward a homophony music score aesthetic model to objectively evaluate the quality of music score as a baseline model; second, we put forward eight basic music features and four music aesthetic features.

Related papers

• 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)
• An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music Recommendation [20.164044758068634]
  subjective evaluation is still the most effective form of evaluating artistic works. While compared to music produced by humans, AI generated music still sounds mechanical, monotonous, and lacks aesthetic appeal. We use Birkhoff's aesthetic measure to design a aesthetic model, objectively measuring the aesthetic beauty of music, and form a recommendation list according to the aesthetic feeling of music.
  arXiv  Detail & Related papers  (2024-02-13T09:03:03Z)
• MusicRL: Aligning Music Generation to Human Preferences [62.44903326718772]
  MusicRL is the first music generation system finetuned from human feedback. We deploy MusicLM to users and collect a substantial dataset comprising 300,000 pairwise preferences. We train MusicRL-U, the first text-to-music model that incorporates human feedback at scale.
  arXiv  Detail & Related papers  (2024-02-06T18:36:52Z)
• A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music [14.453416870193072]
  This study aims to comprehensively evaluate the subjective, objective, and combined methodologies for assessing AI-generated music. Ultimately, this study provides a valuable reference for unifying generative AI in the field of music evaluation.
  arXiv  Detail & Related papers  (2023-08-26T02:44:33Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• RMSSinger: Realistic-Music-Score based Singing Voice Synthesis [56.51475521778443]
  RMS-SVS aims to generate high-quality singing voices given realistic music scores with different note types. We propose RMSSinger, the first RMS-SVS method, which takes realistic music scores as input. In RMSSinger, we introduce word-level modeling to avoid the time-consuming phoneme duration annotation and the complicated phoneme-level mel-note alignment.
  arXiv  Detail & Related papers  (2023-05-18T03:57:51Z)
• An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance [8.751312368054016]
  subjective evaluation is still a ultimate method of music aesthetics research. The music performance generated by AI is still mechanical, monotonous and lacking in beauty. This paper uses Birkhoff's aesthetic measure to propose a method of objective measurement of beauty.
  arXiv  Detail & Related papers  (2023-04-23T03:02:24Z)
• Evaluating Deep Music Generation Methods Using Data Augmentation [13.72212417973239]
  We focus on a homogeneous, objective framework for evaluating samples of algorithmically generated music. We do not seek to assess the musical merit of generated music, but instead explore whether generated samples contain meaningful information pertaining to emotion or mood/theme.
  arXiv  Detail & Related papers  (2021-12-31T20:35:46Z)
• 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)
• Music Gesture for Visual Sound Separation [121.36275456396075]
  "Music Gesture" is a keypoint-based structured representation to explicitly model the body and finger movements of musicians when they perform music. We first adopt a context-aware graph network to integrate visual semantic context with body dynamics, and then apply an audio-visual fusion model to associate body movements with the corresponding audio signals.
  arXiv  Detail & Related papers  (2020-04-20T17:53:46Z)
• 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.