A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Less talk, more rock: automated organization of community-contributed collections of concert videos
Proceedings of the 18th international conference on World wide web
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Adapting boosting for information retrieval measures
Information Retrieval
Objective Assessment of Speech and Audio Quality—Technology and Applications
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PEMO-Q—A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception
IEEE Transactions on Audio, Speech, and Language Processing
P.563—The ITU-T Standard for Single-Ended Speech Quality Assessment
IEEE Transactions on Audio, Speech, and Language Processing
MoViMash: online mobile video mashup
Proceedings of the 20th ACM international conference on Multimedia
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Immensely popular video sharing websites such as YouTube have become the most important sources of music information 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 recording device. However, most video search engines do not take audio quality into consideration when retrieving and ranking results. Given the fact that most users prefer live music videos with better audio quality, we propose the first automatic, non-reference audio quality assessment framework 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 second contains 2,400 synthetic pieces systematically generated by adding noise effects to clean recordings. Then, we formulate 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 framework significantly improves the ranking performance of live music recording retrieval and can prove useful for various real-world music applications.