Developing a personalised Music Affect Recommender System.

该项目旨在开发一个个性化的音乐推荐系统，使用感知音质，影响和喜爱。使用事先口头注释和评级的推荐系统很常见（亚马逊），但不适合不熟悉的艺术家创作的不太流行的音乐，因为缺乏社交使用数据。该项目旨在建立对音乐情感的感知及其与响度和音质的关系；以及通过标签和声学内容组织数字图书馆的自动化。在此基础上，该项目试图创建一个模型，根据声音内容、影响和喜欢来给出个人偏好的建议。该系统将被设计为快速更新，并鼓励探索熟悉和不熟悉的音乐。

The project aims to develop a personalised music recommender system using perceived tone quality, affect and liking. Recommender systems using prior verbal annotations and ratings are common (Amazon) but inappropriate for less popular music by unfamiliar artists, which lacks social use data. The project intends to build on work into perception of musical affect and its relation to loudness and tone quality; and the automation of the organisation of digital libraries both by labels and acoustic content. Developing this, the project seeks to create a model that gives recommendations which accounts for an individual's preferences based on acoustic content, affect and liking. The system will be designed to update rapidly and to encourage exploration of familiar and unfamiliar music.