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Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices(More)
Social tagging can provide rich semantic information for large-scale retrieval in music discovery. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic(More)
Composition, listening, and performance are essential activities in classroom music education, yet conventional music classes impose unnecessary limitations on students' ability to develop these skills. Based on in-depth fieldwork and a user-centered design approach, we created MOGCLASS, a multimodal collaborative music environment that enhances students'(More)
Existing content-based music recommendation systems typically employ a \textit{two-stage} approach. They first extract traditional audio content features such as Mel-frequency cepstral coefficients and then predict user preferences. However, these traditional features, originally not created for music recommendation, cannot capture all relevant information(More)
Current music recommender systems typically act in a greedy manner by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend <i>novel</i> songs that are potentially interesting. A successful recommender system must(More)
We introduce MOGCLASS: a system of networked mobile devices to amplify and extend children's capabilities to perceive, perform and produce music collaboratively in classroom context. MOGCLASS includes various features for students to enhance their motivation, interest, and collaboration in music class. It provides a wide-ranging palette of easy-to-use(More)
Collaborative filtering (CF) techniques have shown great success in music recommendation applications. However, traditional collaborative-filtering music recommendation algorithms work in a greedy way, invariably recommending songs with the highest predicted user ratings. Such a purely exploitative strategy may result in suboptimal performance over the long(More)
Existing music recommender systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we demonstrate in this demo a novel system to employ contextual information collected with mobile devices for(More)
With the rapid pace of modern life, millions of people suffer from sleep problems. Music therapy, as a non-medication approach to mitigating sleep problems, has attracted increasing attention recently. However the adaptability of music therapy is limited by the time consuming task of choosing suitable music for users. Inspired by this observation, we(More)
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