In-depth analysis of music structure as a text network
- URL: http://arxiv.org/abs/2303.13631v2
- Date: Tue, 2 Jan 2024 09:35:33 GMT
- Title: In-depth analysis of music structure as a text network
- Authors: Ping-Rui Tsai, Yen-Ting Chou, Nathan-Christopher Wang, Hui-Ling Chen,
Hong-Yue Huang, Zih-Jia Luo, and Tzay-Ming Hong
- Abstract summary: We focus on the fundamental elements of music and construct an evolutionary network from the perspective of music as a natural language.
We aim to comprehend the structural differences in music across different periods, enabling a more scientific exploration of music.
- Score: 7.735597173716555
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Music, enchanting and poetic, permeates every corner of human civilization.
Although music is not unfamiliar to people, our understanding of its essence
remains limited, and there is still no universally accepted scientific
description. This is primarily due to music being regarded as a product of both
reason and emotion, making it difficult to define. In this article, we focus on
the fundamental elements of music and construct an evolutionary network from
the perspective of music as a natural language, aligning with the statistical
characteristics of texts. Through this approach, we aim to comprehend the
structural differences in music across different periods, enabling a more
scientific exploration of music. Relying on the advantages of structuralism, we
can concentrate on the relationships and order between the physical elements of
music, rather than getting entangled in the blurred boundaries of science and
philosophy. The scientific framework we present not only conforms to past
conclusions in music, but also serves as a bridge that connects music to
natural language processing and knowledge graphs.
Related papers
- 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) - 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) - ChatMusician: Understanding and Generating Music Intrinsically with LLM [81.48629006702409]
ChatMusician is an open-source Large Language Models (LLMs) that integrates intrinsic musical abilities.
It can understand and generate music with a pure text tokenizer without any external multi-modal neural structures or tokenizers.
Our model is capable of composing well-structured, full-length music, conditioned on texts, chords, melodies, motifs, musical forms, etc.
arXiv Detail & Related papers (2024-02-25T17:19:41Z) - Exploring and Applying Audio-Based Sentiment Analysis in Music [0.0]
The ability of a computational model to interpret musical emotions is largely unexplored.
This study seeks to (1) predict the emotion of a musical clip over time and (2) determine the next emotion value after the music in a time series to ensure seamless transitions.
arXiv Detail & Related papers (2024-02-22T22:34:06Z) - Are Words Enough? On the semantic conditioning of affective music
generation [1.534667887016089]
This scoping review aims to analyze and discuss the possibilities of music generation conditioned by emotions.
In detail, we review two main paradigms adopted in automatic music generation: rules-based and machine-learning models.
We conclude that overcoming the limitation and ambiguity of language to express emotions through music has the potential to impact the creative industries.
arXiv Detail & Related papers (2023-11-07T00:19:09Z) - A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data.
The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
arXiv Detail & Related papers (2022-11-21T14:15:43Z) - Affective Idiosyncratic Responses to Music [63.969810774018775]
We develop methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform.
We test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses.
arXiv Detail & Related papers (2022-10-17T19:57:46Z) - 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 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) - Structural characterization of musical harmonies [4.416484585765029]
We use a hybrid method in which an evidence-gathering numerical method detects modulation and then, based on the detected tonalities, a non-ambiguous grammar can be used for analyzing the structure of each tonal component.
Experiments with music from the XVII and XVIII centuries show that we can detect the precise point of modulation with an error of at most two chords in almost 97% of the cases.
arXiv Detail & Related papers (2019-12-27T23:15:49Z)
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.