Non-reference audio quality assessment for online live music recordings

Abstract

Immensely popular video sharing websites such as YouTube have become the most important sources of music informa- Tion for Internet users and the most prominent platform for sharing live music. The audio quality of this huge amount of live music recordings, however, varies significantly due to factors such as environmental noise, location, and record- ing device. However, most video search engines do not take audio quality into consideration when retrieving and rank- ing results. Given the fact that most users prefer live mu- sic videos with better audio quality, we propose the first automatic, non-reference audio quality assessment frame- work for live music video search online. We first construct two annotated datasets of live music recordings. The first dataset contains 500 human-annotated pieces, and the sec- ond contains 2,400 synthetic pieces systematically generated by adding noise effects to clean recordings. Then, we for- mulate the assessment task as a ranking problem and try to solve it using a learning-based scheme. To validate the effectiveness of our framework, we perform both objective and subjective evaluations. Results show that our frame- work significantly improves the ranking performance of live music recording retrieval and can prove useful for various real-world music applications. Copyright © 2013 ACM.