The Latin Music Database

In this paper we present the Latin Music Database, a novel database of Latin musical recordings which has been developed for automatic music genre classification, but can also be used in other music information retrieval tasks. The method for assigning genres to the musical recordings is based on human expert perception and therefore capture their tacit knowledge in the genre labeling process. We also present the ethnomusicology of the genres available in the database as it might provide important information for the analysis of the results of any experiment that employs the database

Evaluating Collaborative Filtering Algorithms for Music Recommendations on Chinese Music Data

In this thesis, I explored Collaborative Filtering algorithms used in music recommendation tasks in the Music Information Retrieval field. To find out if those CF algorithms work on Chinese music data, I developed a new dataset from the mainstream Chinese music streaming platform NetEase Could Music, and compared the performance of a series of Memory-based and Model-based collaborative filtering algorithms on our dataset. Our experimental results prove that these CF algorithms aiming at users’ information are effective on our dataset, and they have the predictive ability of music recommendation tasks on Chinese music data. In general, Model-based algorithms perform better than Memory-based algorithms. Within them, the SVD++ algorithm from Matrix Factorization-based methods reaches the best overall accuracy.Bachelor of Scienc

Fusion multi-niveaux par boosting pour le tagging automatique

Tags constitute a very useful tool for multimedia document indexing. This PhD thesis deals with automatic tagging, which consists in associating a set of tags to each song automatically, using an algorithm. We use boosting techniques to design a learning which better considers the complexity of the information expressed by music. A boosting algorithm is proposed, which can jointly use song descriptions associated to excerpts of different durations. This algorithm is used to fuse new descriptions, which belong to different abstraction levels. Finally, a new learning framework is proposed for automatic tagging, which better leverages the subtlety ofthe information expressed by music.Les tags constituent un outil très utile pour indexer des documents multimédias. Cette thèse de doctorat s’intéresse au tagging automatique, c’est à dire l’association automatique par un algorithme d’un ensemble de tags à chaque morceau. Nous utilisons des techniques de boosting pour réaliser un apprentissage prenant mieux en compte la richesse de l’information exprimée par la musique. Un algorithme de boosting est proposé, afin d’utiliser conjointement des descriptions de morceaux associées à des extraits de différentes durées. Nous utilisons cet algorithme pour fusionner de nouvelles descriptions, appartenant à différents niveaux d’abstraction. Enfin, un nouveau cadre d’apprentissage est proposé pour le tagging automatique, qui prend mieux en compte les subtilités des associations entre les tags et les morceaux