Cover songs are alternate versions of a song by a different artist. Long
being a vital part of the music industry, cover songs significantly influence
music culture and are commonly heard in public venues. The rise of online music
platforms has further increased their prevalence, often as background music or
video soundtracks. While current automatic identification methods serve
adequately for original songs, they are less effective with cover songs,
primarily because cover versions often significantly deviate from the original
compositions. In this paper, we propose a novel method for cover song detection
that utilizes the lyrics of a song. We introduce a new dataset for cover songs
and their corresponding originals. The dataset contains 5078 cover songs and
2828 original songs. In contrast to other cover song datasets, it contains the
annotated lyrics for the original song and the cover song. We evaluate our
method on this dataset and compare it with multiple baseline approaches. Our
results show that our method outperforms the baseline approaches.

本文提出了一种利用歌词进行翻唱歌曲检测的新方法，通过引入一份包含 5078 首翻唱歌曲和 2828 首原创歌曲的数据集，并对该方法进行评估和与基准方法的比较，结果表明该方法优于基准方法。

封面歌曲检测中的创新：基于歌词的方法

Innovations in Cover Song Detection: A Lyrics-Based Approach

Machine Learning models are being utilized extensively to drive recommender
systems, which is a widely explored topic today. This is especially true of the
music industry, where we are witnessing a surge in growth. Besides a large
chunk of active users, these systems are fueled by massive amounts of data.
These large-scale systems yield applications that aim to provide a better user
experience and to keep customers actively engaged. In this paper, a distributed
Machine Learning (ML) pipeline is delineated, which is capable of taking a
subset of songs as input and producing a new subset of songs identified as
being similar to the inputted subset. The publicly accessible Million Songs
Dataset (MSD) enables researchers to develop and explore reasonably efficient
systems for audio track analysis and recommendations, without having to access
a commercialized music platform. The objective of the proposed application is
to leverage an ML system trained to optimally recommend songs that a user might
like.

通过使用机器学习模型及百万曲目数据集，本文提出了一个音乐推荐系统，旨在优化为用户推荐喜欢的歌曲。

Related Rhythms: Recommendation System To Discover Music You May Like

One of the most intriguing debates regarding a novel task is the development
of search engines and recommendation-based systems in the music industry.
Studies have shown a drastic depression in the search engine fields, due to
concerning factors such as speed, accuracy and the format of data given for
querying. Often people face difficulty in searching for a song solely based on
the title, hence a solution is proposed to complete a search analysis through a
single query input and is matched with the lyrics of the songs present in the
database. Hence it is essential to incorporate cutting-edge technology tools
for developing a user-friendly search engine. Jina AI is an MLOps framework for
building neural search engines that are utilized, in order for the user to
obtain accurate results. Jina AI effectively helps to maintain and enhance the
quality of performance for the search engine for the query given. An effective
search engine and a recommendation system for the music industry, built with
JinaAI.

在音乐界的搜索引擎和推荐系统开发过程中，Jina AI 是一种用于构建神经搜索引擎的 MLOps 框架，它有效帮助维护和提升查询给出的搜索引擎和推荐系统的性能质量。

音乐行业的搜索引擎和推荐系统 —— 基于 JinaAI 的构建

Search Engine and Recommendation System for the Music Industry built  with JinaAI

The integration of artificial intelligence (AI) technology in the music
industry is driving a significant change in the way music is being composed,
produced and mixed. This study investigates the current state of AI in the
mixing workflows and its adoption by different user groups. Through
semi-structured interviews, a questionnaire-based study, and analyzing web
forums, the study confirms three user groups comprising amateurs, pro-ams, and
professionals. Our findings show that while AI mixing tools can simplify the
process and provide decent results for amateurs, pro-ams seek precise control
and customization options, while professionals desire control and customization
options in addition to assistive and collaborative technologies. The study
provides strategies for designing effective AI mixing tools for different user
groups and outlines future directions.

本研究探讨人工智能技术在音乐制作中的应用现状，通过问卷、半结构化访谈及网络论坛的分析，确认了三种使用者群体分别为业余用户、半职业用户和专业用户，发现业余用户可通过人工智能混音工具简化音乐制作过程并获得不错的效果，而半职业用户和专业用户则更追求精准控制和定制化选项，并且对辅助和协作技术有追求，最后提供了为不同使用者群体设计有效人工智能混音工具的策略和未来发展方向。