CAREER: An Integrated Framework for Multimodal Music Search and Discovery

职业：多模式音乐搜索和发现的集成框架

• 批准号: 1054960
• 负责人: Gert Lanckriet
• 金额: $ 55万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054960&HistoricalAwards=false
• 关键词: CAREER; Integrated; Framework; Multimodal; Music

A revolution in music production and distribution has made millions of songs instantly available to virtually anyone, on the Internet. However, a listener looking for "dark electronica with cello" or "music like U2's", without knowing a relevant artist or song name, or a musicologist wanting to search through large amounts of unknown ethnic music, would face serious challenges. Novel music search and discovery technologies are required to help users find the desired content.The non-text-based, multimodal character of Internet-wide information about music (audio clips, lyrics, web documents, images, band networks, etc.) poses a new and difficult challenge to existing database technology that depends on unimodal, text-based data-structures. This project addresses two fundamental research questions at the core of addressing this challenge: (1) The automated annotation of (non-text-based) audio content with descriptive keywords; and (2) the automated integration of the heterogeneous content of multimodal databases, to improve music search and discovery on the Internet or in a personal database. The resulting architecture leverages the automation and scalability of machine learning with the effectiveness of human computation, engaging music professionals or enthusiasts around the world.The research addresses questions at the core of multimedia information retrieval in general, enabling the design of a new generation of expressive and flexible retrieval systems for multimodal databases, with applications to music discovery, video retrieval, indexing multimedia content on the home PC, etc.The results of this project, including a software library and annotated music data sets, will be incorporated in ongoing education and outreach activities and disseminated via the project website (http://cosmal.ucsd.edu/~gert/CAREER.html) to enhance research and education in music information retrieval.

音乐制作和发行的一场革命使数百万首歌曲在互联网上几乎任何人都可以立即获得。然而，一个听众寻找“大提琴的黑暗电子音乐”或“像U2这样的音乐”，而不知道相关的艺术家或歌曲名称，或者一个音乐学家想要搜索大量未知的民族音乐，将面临严重的挑战。需要新的音乐搜索和发现技术来帮助用户找到所需的内容。互联网上关于音乐的信息（音频片段、歌词、网络文档、图像、乐队网络等）的非文本、多模式特征。对依赖于单峰的、基于文本的数据结构的现有数据库技术提出了新的和困难的挑战。该项目解决了两个基本的研究问题，在解决这一挑战的核心：（1）（非文本为基础的）音频内容与描述性关键字的自动注释;（2）多模态数据库的异构内容的自动集成，以提高音乐搜索和发现在互联网上或个人数据库。由此产生的架构利用了机器学习的自动化和可扩展性与人类计算的有效性，吸引了世界各地的音乐专业人士或爱好者。该研究解决了多媒体信息检索的核心问题，使新一代多模态数据库的表达和灵活的检索系统的设计成为可能，并应用于音乐发现，视频检索，该项目的成果，包括一个软件库和注释音乐数据集，将纳入正在进行的教育和推广活动，并通过项目网站（http：//cosmal.ucsd.edu/caregert/CAREER.html）传播，以加强音乐信息检索方面的研究和教育。