Computational aesthetic evaluation has made remarkable contribution to visual
art works, but its application to music is still rare. Currently, subjective
evaluation is still the most effective form of evaluating artistic works.
However, subjective evaluation of artistic works will consume a lot of human
and material resources. The popular AI generated content (AIGC) tasks nowadays
have flooded all industries, and music is no exception. While compared to music
produced by humans, AI generated music still sounds mechanical, monotonous, and
lacks aesthetic appeal. Due to the lack of music datasets with rating
annotations, we have to choose traditional aesthetic equations to objectively
measure the beauty of music. In order to improve the quality of AI music
generation and further guide computer music production, synthesis,
recommendation and other tasks, 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. Experiments
show that our objective aesthetic model and recommendation method are
effective.

通过使用 Birkhoff 的美学度量来设计一种客观测量音乐美感的美学模型，并根据音乐的美感形成推荐列表，以提高人工智能音乐生成的质量和指导计算机音乐制作、合成和推荐等任务的研究。

审美感知音乐推荐的排序复杂度审美评估模型

An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music  Recommendation

The semantic description of music metadata is a key requirement for the
creation of music datasets that can be aligned, integrated, and accessed for
information retrieval and knowledge discovery. It is nonetheless an open
challenge due to the complexity of musical concepts arising from different
genres, styles, and periods -- standing to benefit from a lingua franca to
accommodate various stakeholders (musicologists, librarians, data engineers,
etc.). To initiate this transition, we introduce the Music Meta ontology, a
rich and flexible semantic model to describe music metadata related to artists,
compositions, performances, recordings, and links. We follow eXtreme Design
methodologies and best practices for data engineering, to reflect the
perspectives and the requirements of various stakeholders into the design of
the model, while leveraging ontology design patterns and accounting for
provenance at different levels (claims, links). After presenting the main
features of Music Meta, we provide a first evaluation of the model, alignments
to other schema (Music Ontology, DOREMUS, Wikidata), and support for data
transformation.

音乐元本体论介绍了一种丰富而灵活的语义模型，用于描述与艺术家、作曲、演奏、录音和链接相关的音乐元数据，并通过数据工程的最佳实践来考虑各个利益相关者的需求和角度，从而支持数据对齐、转化和检索。

音乐元本体论：音乐元数据的互操作性的灵活语义模型

The Music Meta Ontology: a flexible semantic model for the  interoperability of music metadata

Music datasets play a crucial role in advancing research in machine learning
for music. However, existing music datasets suffer from limited size,
accessibility, and lack of audio resources. To address these shortcomings, we
present DISCO-10M, a novel and extensive music dataset that surpasses the
largest previously available music dataset by an order of magnitude. To ensure
high-quality data, we implement a multi-stage filtering process. This process
incorporates similarities based on textual descriptions and audio embeddings.
Moreover, we provide precomputed CLAP embeddings alongside DISCO-10M,
facilitating direct application on various downstream tasks. These embeddings
enable efficient exploration of machine learning applications on the provided
data. With DISCO-10M, we aim to democratize and facilitate new research to help
advance the development of novel machine learning models for music.

DISCO-10M 是一份新的 extensive music dataset，以超过以往已有的 music dataset 数量为目标，在 multi-stage filtering process 中实现了相似度比较，并提供预算的 CLAP embeddings，以便于直接用于各种 downstream tasks。

DISCO-10M: 一个大规模音乐数据集

DISCO-10M: A Large-Scale Music Dataset

The annotation of music content is a complex process to represent due to its
inherent multifaceted, subjectivity, and interdisciplinary nature. Numerous
systems and conventions for annotating music have been developed as independent
standards over the past decades. Little has been done to make them
interoperable, which jeopardises cross-corpora studies as it requires users to
familiarise with a multitude of conventions. Most of these systems lack the
semantic expressiveness needed to represent the complexity of the musical
language and cannot model multi-modal annotations originating from audio and
symbolic sources. In this article, we introduce the Music Annotation Pattern,
an Ontology Design Pattern (ODP) to homogenise different annotation systems and
to represent several types of musical objects (e.g. chords, patterns,
structures). This ODP preserves the semantics of the object's content at
different levels and temporal granularity. Moreover, our ODP accounts for
multi-modality upfront, to describe annotations derived from different sources,
and it is the first to enable the integration of music datasets at a large
scale.

介绍了一种音乐注释模式，即本体设计模式，用于归一化不同注释系统并表示多种类型的音乐对象，并解决了多模态注释源的集成问题，并为大规模音乐数据集的集成提供了解决方案。