Music Information Retrieval for Irish Traditional Music Automatic Analysis of Harmonic, Rhythmic, and Melodic Features for Efficient Key-Invariant Tune Recognition

Music making and listening practices increasingly rely on techno logy,and,asaconsequence,techniquesdevelopedinmusicinformation retrieval (MIR) research are more readily available to end users, in par ticular via online tools and smartphone apps. However, the majority of MIRresearchfocusesonWesternpopandclassicalmusic,andthusdoes not address specificities of other musical idioms. Irishtraditionalmusic(ITM)ispopularacrosstheglobe,withregular sessionsorganisedonallcontinents. ITMisadistinctivemusicalidiom, particularly in terms of heterophony and modality, and these character istics can constitute challenges for existing MIR algorithms. The bene fitsofdevelopingMIRmethodsspecificallytailoredtoITMisevidenced by Tunepal, a query-by-playing tool that has become popular among ITM practitioners since its release in 2009. As of today, Tunepal is the state of the art for tune recognition in ITM. The research in this thesis addresses existing limitations of Tunepal. The main goal is to find solutions to add key-invariance to the tune re cognitionsystem,animportantfeaturethatiscurrentlymissinginTune pal. Techniques from digital signal processing and machine learning are used and adapted to the specificities of ITM to extract harmonic iv and temporal features, respectively with improvements on existing key detection methods, and a novel method for rhythm classification. These featuresarethenusedtodevelopakey-invarianttunerecognitionsystem that is computationally efficient while maintaining retrieval accuracy to a comparable level to that of the existing system

Graph based representation of the music symbolic level. A music information retrieval application

In this work, a new music symbolic level representation system is described. It has been tested in two information retrieval tasks concerning similarity between segments of music and genre detection of a given segment. It could include both harmonic and contrapuntal informations. Moreover, a new large dataset consisting of more than 5000 leadsheets is presented, with meta informations taken from different web databases, including author information, year of first performance, lyrics, genre, etc.ope

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

Information-Theoretic Measures of Predictability for Music Content Analysis.

PhDThis thesis is concerned with determining similarity in musical audio, for the purpose of applications in music content analysis. With the aim of determining similarity, we consider the problem of representing temporal structure in music. To represent temporal structure, we propose to compute information-theoretic measures of predictability in sequences. We apply our measures to track-wise representations obtained from musical audio; thereafter we consider the obtained measures predictors of musical similarity. We demonstrate that our approach benefits music content analysis tasks based on musical similarity. For the intermediate-specificity task of cover song identification, we compare contrasting discrete-valued and continuous-valued measures of pairwise predictability between sequences. In the discrete case, we devise a method for computing the normalised compression distance (NCD) which accounts for correlation between sequences. We observe that our measure improves average performance over NCD, for sequential compression algorithms. In the continuous case, we propose to compute information-based measures as statistics of the prediction error between sequences. Evaluated using 300 Jazz standards and using the Million Song Dataset, we observe that continuous-valued approaches outperform discrete-valued approaches. Further, we demonstrate that continuous-valued measures of predictability may be combined to improve performance with respect to baseline approaches. Using a filter-and-refine approach, we demonstrate state-of-the-art performance using the Million Song Dataset. For the low-specificity tasks of similarity rating prediction and song year prediction, we propose descriptors based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. We evaluate our descriptors using a dataset of 15 500 track excerpts of Western popular music, for which we have 7 800 web-sourced pairwise similarity ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.This work was supported by a UK EPSRC DTA studentship

Lainakappaleiden tunnistaminen tiedon tiivistämiseen perustuvia etäisyysmittoja käyttäen

Measuring similarity in music data is a problem with various potential applications. In recent years, the task known as cover song identification has gained widespread attention. In cover song identification, the purpose is to determine whether a piece of music is a different rendition of a previous version of the composition. The task is quite trivial for a human listener, but highly challenging for a computer. This research approaches the problem from an information theoretic starting point. Assuming that cover versions share musical information with the original performance, we strive to measure the degree of this common information as the amount of computational resources needed to turn one version into another. Using a similarity measure known as normalized compression distance, we approximate the non-computable Kolmogorov complexity as the length of an object when compressed using a real-world data compression algorithm. If two pieces of music share musical information, we should be able to compress one using a model learned from the other. In order to use compression-based similarity measuring, the meaningful musical information needs to be extracted from the raw audio signal data. The most commonly used representation for this task is known as chromagram: a sequence of real-valued vectors describing the temporal tonal content of the piece of music. Measuring the similarity between two chromagrams effectively with a data compression algorithm requires further processing to extract relevant features and find a more suitable discrete representation for them. Here, the challenge is to process the data without losing the distinguishing characteristics of the music. In this research, we study the difficult nature of cover song identification and search for an effective compression-based system for the task. Harmonic and melodic features, different representations for them, commonly used data compression algorithms, and several other variables of the problem are addressed thoroughly. The research seeks to shed light on how different choices in the scheme attribute to the performance of the system. Additional attention is paid to combining different features, with several combination strategies studied. Extensive empirical evaluation of the identification system has been performed, using large sets of real-world music data. Evaluations show that the compression-based similarity measuring performs relatively well but fails to achieve the accuracy of the existing solution that measures similarity by using common subsequences. The best compression-based results are obtained by a combination of distances based on two harmonic representations obtained from chromagrams using hidden Markov model chord estimation, and an octave-folded version of the extracted salient melody representation. The most distinct reason for the shortcoming of the compression performance is the scarce amount of data available for a single piece of music. This was partially overcome by internal data duplication. As a whole, the process is solid and provides a practical foundation for an information theoretic approach for cover song identification.Lainakappeleiksi kutsutaan musiikkiesityksiä, jotka ovat eri esittäjän tekemiä uusia tulkintoja kappaleen alkuperäisen esittäjän tekemästä versiosta. Toisinaan lainakappaleet voivat olla hyvinkin samanlaisia alkuperäisversioiden kanssa, toisinaan versioilla saattaa olla vain nimellisesti yhtäläisyyksiä. Ihmisille lainakappaleiden tunnistaminen on yleensä helppoa, jos alkuperäisesitys on tuttu. Lainakappaleiden automaattinen, algoritmeihin perustuva tunnistaminen on kuitenkin huomattavasti haastavampi ongelma, eikä täysin tyydyttäviä ratkaisuja ole vielä esitetty. Ongelman ratkaisulla olisi useita tutkimuksellisesti ja kaupallisesti potentiaalisia sovelluskohteita, kuten esimerkiksi plagioinnin automaattinen tunnistaminen. Väitöskirjassa lainakappeleiden automaattista tunnistamista käsitellään informaatioteoreettisesta lähtökohdasta. Tutkimuksessa selvitetään, pystytäänkö kappaleiden sisältämää tonaalista samanlaisuutta mittaamaan siten, että sen perusteella voidaan todeta eri esitysten olevan pohjimmiltaan saman sävellyksen eri tulkintoja. Samanlaisuuden mittaamisessa hyödynnetään tiedontiivistysalgoritmeihin perustuvaa samanlaisuusmetriikkaa, jota varten musiikkikappaleista pitää pystyä erottamaan ja esittämään sen sävellyksellisesti yksilöivimmät piirteet. Tutkimus tehdään laajalla aineistolla audiomuotoista populaarimusiikkia. Väitöstutkimus käy läpi useita tutkimusongelman eri vaiheita lähtien signaalidatan käsittelemiseen liittyvistä parametreista, edeten siihen miten signaalista erotettu esitysmuoto saadaan muunnettua merkkijonomuotoiseksi siten, että prosessin tulos edelleen kuvaa kappaleen keskeisiä musiikillisia piirteitä, ja miten saatua merkkijonodataa voidaan vielä jatkokäsitellä tunnistamisen parantamiseksi. Tämän ohella väitöksessä tutkitaan, miten kappaleiden erilaiset musiikilliset eroavaisuudet (tempo, sävellaji, sovitukset) vaikuttavat tunnistamiseen ja miten näiden eroavaisuuksien vaikutus mittaamisessa voidaan minimoida. Tutkimuksen kohteena on myös yleisimpien tiedontiivistysalgoritmien soveltuvuus mittausmenetelmänä käsiteltävään ongelmaan. Näiden lisäksi tutkimus esittelee, miten samasta kappaleesta irrotettuja useita erilaisia esitysmuotoja voidaan yhdistää paremman tunnistamistarkkuuden saavuttamiseksi. Lopputuloksena väitöskirja esittelee tiedontiivistystä hyödyntävän järjestelmän lainakappaleiden tunnistamiseen ja käsittelee sen keskeiset vahvuudet ja heikkoudet. Tutkimuksen tuloksena arvioidaan myös mitkä asiat tekevät lainakappaleiden automaattisesta tunnistamisesta niin haastavan ongelman kuin mitä se on