SBIR Phase I: The Echo Nest Music Personalization

SBIR 第一阶段：Echo Nest 音乐个性化

• 批准号: 0637918
• 负责人: Tristan Jehan
• 金额: --
• 依托单位: The Echo Nest Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0637918&HistoricalAwards=false
• 关键词: SBIR; Phase; Echo; Nest; Music

This Small Business Innovation Research (SBIR) Phase I project aims at solving the computational problem of personalizing music search and recommendation. The recent explosion of digital music has created an urgent need for powerful knowledge management techniques and tools. Because of the highly subjective nature of musical content and perception, the best possible search strategy would rank media in a personalized fashion, based on each individual's tastes and preferences, from combined cultural and acoustic descriptions. The Echo Nest's predictive personalization technology computes and collects, collaboratively and automatically, cultural opinions online and acoustic content using unsupervised data mining and machine listening techniques. Combining cultural and acoustic notions of music together with the analysis of an individual's listening patterns, ratings and feedback, leads to a vertical search/recommendation engine that knows about content, communities' reaction, and users' preferences. Intelligent music personalization goes beyond search and recommendation. Because the approach is fully autonomous and scalable it can efficiently address the long tail of independent music as well as the Billboard 100; discover artists and niches or predict trends and hits; market indies directly to individuals and optimize aggregators, distributors, and record labels' selection. The Echo Nest engine is the perceptual-media complement to purely text-based search engines and has a significant market potential.

这个小型企业创新研究(SBIR)第一阶段项目旨在解决个性化音乐搜索和推荐的计算问题。最近数字音乐的爆炸性增长产生了对强大的知识管理技术和工具的迫切需求。由于音乐内容和感知的高度主观性，最好的搜索策略将根据每个人的品味和偏好，从文化和声音描述的组合中，以个性化的方式对媒体进行排名。Echo Nest的预测性个性化技术使用无监督数据挖掘和机器聆听技术，以协作和自动的方式计算和收集在线文化意见和声音内容。将音乐的文化和声学概念与对个人收听模式、评级和反馈的分析结合在一起，产生了一个垂直搜索/推荐引擎，它了解内容、社区的反应和用户的偏好。智能音乐个性化超越了搜索和推荐。由于这种方法是完全自主和可扩展的，它可以有效地解决独立音乐和公告牌100的长尾问题；发现艺术家和利基市场或预测趋势和热门；直接向个人营销印度音乐，并优化聚合器、发行商和唱片公司的选择。Echo Nest引擎是纯粹基于文本的搜索引擎的感知媒体补充，具有巨大的市场潜力。