Intelligent automatic music curator and recommender system

点击翻译按钮获取中文摘要

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.