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 in the audio and thus put a cap on recommendation performance. Using a novel model based on deep belief network and probabilistic graphical model, we unify the two stages into an automated process that simultaneously learns features from audio content and makes personalized recommendations. Compared with existing deep learning based models, our model outperforms them in both the warm-start and cold-start stages without relying on collaborative filtering (CF). We then present an efficient hybrid method to seamlessly integrate the automatically learnt features and CF. Our hybrid method not only significantly improves the performance of CF but also outperforms the traditional feature mbased hybrid method.

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 novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This article presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task. To learn user preferences, it uses a Bayesian model that accounts for both audio content and the novelty of recommendations. A piecewise-linear approximation to the model and a variational inference algorithm help to speed up Bayesian inference. One additional benefit of our approach is a single unified model for both music recommendation and playlist generation. We demonstrate the strong potential of the proposed approach with simulation results and a user study.

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 for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.

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 satisfying users' short-term music playing needs. In our system, contextual information is integrated with music content analysis to offer recommendation for daily activities.

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' musical experience and improves teachers' management of the classroom.

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 discuss the concept of a domain specific music recommendation system, which automatically recommends music for users according to their sleep quality. The proposed system requires multidisciplinary efforts including automated sleep quality measurement and content-based music similarity measure. As a first step, we focus on the automated sleep quality measurement in this paper. An EEG-based approach is proposed to measure user's sleep quality. The advantages of our approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than six sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing Electroencephalography (EEG) signal only, so the user experience is improved because he is attached with fewer sensors during sleep. We conduct experiments based on a standard data set. Our approach achieves high accuracy and shows promising potential for the music recommendation system.

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 of significance for researchers. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. Many of these are underrepresented by current tag recommenders. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensuring attribute diversity. Once the user uploads or browses a song, the system recommends a list of relevant tags in each attribute independently. To the best of our knowledge, this is the first method to consider Explicit Multiple Attributes for tag recommendation. Our system is designed for large-scale deployment, on the order of millions of objects. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. We evaluate our tag recommendation system on CAL-500 and a large-scale data set ($N = 77,448$ songs) generated by crawling Youtube and Last.fm. Our results indicate that our proposed method is both effective for recommending attribute-diverse relevant tags and efficient at scalable processing.

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 musical instruments for students to choose from, and supports both collaborative silent practice with headphones, and collaborative performance with loudspeakers. To facilitate classroom management, the teacher's interface is used to control students' activities. Our evaluation results indicate that MOGCLASS is effective in increasing students' motivation in learning music and in supporting teachers' classroom management