Data Set Used
In the field of Music Information Retrieval (MIR), multi-label genre classification is the problem of assigning one or more genre labels to a music piece. In this work, we propose a set of ensemble techniques, which are specific to the task of multi-label genre classification. Our goal is to enhance classification performance by combining multiple… (More)
In this paper we study the problem of automatic music tag annotation. Treating tag annotation as a computational classification process, we attempt to explore the relationship between acoustic features and music tags. Toward this end, we conduct a series of empirical experiments to evaluate a set of multi-label classifiers and demonstrate which ones are… (More)
Large-scale distributed systems must be built to anticipate and mitigate a variety of hardware and software failures. In order to build confidence that fault-tolerant systems are correctly implemented, Netflix (and similar enterprises) regularly run failure drills in which faults are deliberately injected in their production system. The combinatorial space… (More)
Music tags provide descriptive and rich information about a music piece, including its genre, artist, emotion, instrument , etc. While many work on automating it, at present, tag annotation is largely a manual process. It often involves judgements and opinions from people of different background and level of musical expertise. Therefore, the resulting tags… (More)
In this paper, we present a brief overview of the design decisions and characteristics of CAMEL (Content-based Audio and Music Extraction Library), an easy-to-use C++ framework developed for content-based audio and music analysis. The framework provides a set of tools that are suitable for a wide range of analysis tasks. At the heart of the framework is a… (More)
In this work, we study the problem of genre prediction on music data. The prediction is based on a genre map, which is constructed from clustering training music data. We make use of a novel algorithm which captures the structural distances from music data and achieves a high clustering accuracy. Preliminary experiments are conducted and discussed.