Enabling Embodied Analogies in Intelligent Music Systems

The present methodology is aimed at cross-modal machine learning and uses multidisciplinary tools and methods drawn from a broad range of areas and disciplines, including music, systematic musicology, dance, motion capture, human-computer interaction, computational linguistics and audio signal processing. Main tasks include: (1) adapting wisdom-of-the-crowd approaches to embodiment in music and dance performance to create a dataset of music and music lyrics that covers a variety of emotions, (2) applying audio/language-informed machine learning techniques to that dataset to identify automatically the emotional content of the music and the lyrics, and (3) integrating motion capture data from a Vicon system and dancers performing on that music.Comment: 4 page

On the Importance of Considering Country-specific Aspects on the Online-Market: An Example of Music Recommendation Considering Country-Specific Mainstream

In the field of music recommender systems, country-specific aspects have received little attention, although it is known that music perception and preferences are shaped by culture; and culture varies across countries. Based on the LFM-1b dataset (including 53,258 users from 47 countries), we show that there are significant country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. Results indicate that, for instance, Finnish users’ listening behavior is farther away from the global mainstream, while United States’ listeners are close to the global mainstream. Relying on rating prediction experiments, we tailor recommendations to a user’s level of preference for mainstream (defined on a global level and on a country level) and the user’s country. Results suggest that, in terms of rating prediction accuracy, a combination of these two filtering strategies works particularly well for users of countries far away from the global mainstream

Effective Music Tagging through Advanced Statistical Modeling

10.1145/1835449.1835555SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval635-64

Modeling Concept Dynamics for Large Scale Music Search

10.1145/2348283.2348346SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval455-46

A content-based music recommender system

Music recommenders have become increasingly relevant due to increased accessibility provided by various music streaming services. Some of these streaming services, such as Spotify, include a recommender system of their own. Despite many advances in recommendation techniques, recommender systems still often do not provide accurate recommendations. This thesis provides an overview of the history and developments of music information retrieval from a more content-based perspective. Furthermore, this thesis describes recommendation as a problem and the methods used for music recommendation with special focus on content-based recommendation by providing detailed descriptions on the audio content features and content-based similarity measures used in content-based music recommender systems. Some of the presented features are used in our own content-based music recommender. Both objective and subjective evaluation of the implemented recommender system further confirm the findings of many researchers that music recommendation based solely on audio content does not provide very accurate recommendations