Intelligent automatic music curator and recommender system

用自然语言描述音乐并非易事。任何这类描述的准确性通常与一个人对该主题的专业水平成正比。因此，使用语言高效搜索音乐的能力通常是音乐家的专利。Apollo Music Store(AMS)专门管理和授权音乐，以满足世界各地广告代理商和品牌的需求。为了保持在其领域的领先地位，AMS正在试验让用户高效地找到和选择相关音乐的新方法。AMS旨在消除将音乐想法翻译成自然语言的需要，取而代之的是允许用户将音乐曲目输入到AMS搜索引擎中作为他们正在寻找的示例。基于我们在Engage Grant名为Content-Aware Music Recommender System Using EMotion Recognition期间获得的结果，其中我们的算法在给定情感上达到了高达87%的准确率，我们打算根据用户提供的音频示例的情感和声学相似性来设计用于音乐自动推荐的机器学习算法。该项目将探索如何为人类情感建模与机器学习技术和音乐理论相结合，因此在情感计算、认知科学和人工智能研究中有着深远的应用。

Describing music with natural language is not easy. The accuracy of any such description is typicallyproportional to one's level of expertise with the subject. The ability to search for music efficiently usinglanguage is therefore typically reserved for musicians.Apollo Music Store (AMS) specializes in curating and licensing music to fulfill the needs of advertisingagencies and brands around the world. To stay at the top of its field, AMS is experimenting with new ways ofletting its users find and select relevant music efficiently. AMS aims to eliminate the need to translate musicalideas to natural language, and instead allow users to input music tracks into the AMS search engine asexamples of what they are looking for.Building on results obtained during our Engage Grant titled Content-aware Music Recommender System usingEmotion Recognition, where our algorithms reached accuracies of up to 87% on given emotions, we intend topursue our research in designing machine-learning algorithms for the automated recommendation of music,based on the emotional and acoustic similarities to an audio example provided by the user. This projectcombines the quest to understand how to model human emotions with machine learning techniques and musictheory, and thus has far reaching applications in affective computing, cognitive science and artificialintelligence research.