Using point-set compression to classify folk songs

Thirteen different compression algorithms were used to calculate the normalized compression distances (NCDs) between pairs of tunes in the Annotated Corpus of 360 Dutch folk songs from the collection Onder de groene linde. These NCDs were then used in conjunction with the 1-nearest-neighbour algorithm and leave-one-out cross-validation to classify the 360 melodies into tune families. The classifications produced by the algorithms were compared with a ground-truth classification prepared by expert musicologists. Twelve of the thirteen compressors used in the ex-periment were based on the discovery of translational equivalence classes (TECs) of maximal translatable patterns (MTPs) in point-set representations of the melodies. The twelve algorithms con-sisted of four variants of each of three basic algorithms, COSI-ATEC, SIATECCOMPRESS and Forth’s algorithm. The main difference between these algorithms is that COSIATEC strictly partitions the input point set into TEC covered sets, whereas the TEC covered sets in the output of SIATECCOMPRESS and Forth’s algorithm may share points. The general-purpose compressor, bzip2, was used as a baseline against which the point-set com-pression algorithms were compared. The highest classification success rate of 77–84 % was achieved by COSIATEC, followed by 60–64 % for Forth’s algorithm and then 52–58 % for SIATE-CCOMPRESS. When the NCDs were calculated using bzip2, the success rate was only 12.5%. The results demonstrate that the effectiveness of NCD for measuring similarity between folk-songs for classification purposes is highly dependent upon the actual compressor chosen. Furthermore, it seems that compres-sors based on finding maximal repeated patterns in point-set rep-resentations of music show more promise for NCD-based mu-sic classification than general-purpose compressors designed for compressing text strings. 1

A new approach to onset detection: towards an empirical grounding of theoretical and speculative ideologies of musical performance

This article assesses aspects of the current state of a project which aims, with the help of computers and computer software, to segment soundfiles of vocal melodies into their component notes, identifying precisely when the onset of each note occurs, and then tracking the pitch trajectory of each note, especially in melodies employing a variety of non-standard temperaments, in which musical intervals smaller than 100 cents are ubiquitous. From there, we may proceed further, to describe many other “micro-features” of each of the notes, but for now our focus is on the onset times and pitch trajectories

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