Music genre classification using traditional and relational approaches

Given the huge size of music collections available on the Web, automatic genre classification is crucial for the organization, search, retrieval and recommendation of music. Different kinds of features have been employed as input to classification models which have been shown to achieve high accuracy in classification scenarios under controlled environments. In this work, we investigate two components of the music genre classification process: a novel feature vector obtained directly from a description of the musical structure described in MIDI files (named as structural features), and the performance of relational classifiers compared to the traditional ones. Neither structural features nor relational classifiers have been previously applied to the music genre classification problem. Our hyphoteses are: (i) the structural features provide a more effective description than those currently employed in automatic music genre classification tasks, and (ii) relational classifiers can outperform traditional algorithms, as they operate on graph models of the data that embed information on the similarity between music tracks. Results from experiments carried out on a music dataset with unbalanced distribution of genres indicate these hypotheses are promising and deserve further investigation.São Paulo Research Foundation (FAPESP) (grants 2011/21880-3, 2011/14165-6, 2011/22749-8, 2012/24537-0 and 2013/12191-5)National Council for Scientific and Technological Development (CNPq) (grant 151836/2013-2

Link prediction in graph construction for supervised and semi-supervised learning

Many real-world domains are relational in nature since they consist of a set of objects related to each other in complex ways. However, there are also flat data sets and if we want to apply graph-based algorithms, it is necessary to construct a graph from this data. This paper aims to: i) increase the exploration of graph-based algorithms and ii) proposes new techniques for graph construction from flat data. Our proposal focuses on constructing graphs using link prediction measures for predicting the existence of links between entities from an initial graph. Starting from a basic graph structure such as a minimum spanning tree, we apply a link prediction measure to add new edges in the graph. The link prediction measures considered here are based on structural similarity of the graph that improves the graph connectivity. We evaluate our proposal for graph construction in supervised and semi-supervised classification and we confirm the graphs achieve better accuracy.São Paulo Research Foundation (FAPESP) (grants: 2013/12191-5, 2011/21880-3 and 2011/22749-8

Methodological contributions by means of machine learning methods for automatic music generation and classification

189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere