Estimating Resemblance of MIDI Documents

Search engines often employ techniques for determining syntactic similarity of web pages. Such a tool allows them to avoid returning multiple copies of essentially the same page when a user makes a query. Here we describe our experience extending these techniques to MIDI music files. The music domain requires modification to cope with problems introduced in the musical setting, such as polyphony. Our experience suggests that when used properly these techniques prove useful for determining duplicates and clustering databases in the musical setting as well.Engineering and Applied Science

Score following: An artificially intelligent musical accompanist

Score Following is the process by which a musician can be tracked through their performance of a piece, for the purpose of accompanying the musician with the appropriate notes. This tracking is done by following the progress of the musician through the score (written music) of the piece, using observations of the notes they are playing. Artificially intelligent musical accompaniment is where a human musician is accompanied by a computer musician. The computer musician is able to produce musical accompaniment that relates musically to the human performance. Hidden Markov Models (HMMs) are a stochastic modelling tool that can be used to represent real-world systems in a variety of domains. This project discusses how HMMs can be used in the domain of Score Following and describes the construction and evaluation of a score following system that uses HMMs to implement score following. It explores the hypothesis that using an HMM to represent a musical score is an efficient and practical way to implement score following, and that in particular this method is suitable for providing real-time accompaniment to a human performer. The score followers developed during this project are tested and compared against other score following systems and against human musicians. The resulting performances support the project hypothesis to a large extent

Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks

Streaming media on the Internet can be unreliable. Services such as audio-on-demand drastically increase the loads on networks; therefore, new, robust, and highly efficient coding algorithms are necessary. One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes into account the semantics and natural repetition of music. Similarity detection within polyphonic audio has presented problematic challenges within the field of music information retrieval. One approach to deal with bursty errors is to use self-similarity to replace missing segments. Many existing systems exist based on packet loss and replacement on a network level, but none attempt repairs of large dropouts of 5 seconds or more. Music exhibits standard structures that can be used as a forward error correction (FEC) mechanism. FEC is an area that addresses the issue of packet loss with the onus of repair placed as much as possible on the listener's device. We have developed a server--client-based framework (SoFI) for automatic detection and replacement of large packet losses on wireless networks when receiving time-dependent streamed audio. Whenever dropouts occur, SoFI swaps audio presented to the listener between a live stream and previous sections of the audio stored locally. Objective and subjective evaluations of SoFI where subjects were presented with other simulated approaches to audio repair together with simulations of replacements including varying lengths of time in the repair give positive results.</jats:p

Analysis and application of hash-based similarity estimation techniques for biological sequence analysis

In Bioinformatics, a large group of problems requires the computation or estimation of sequence similarity. However, the analysis of biological sequence data has, among many others, three capital challenges: a large amount generated data which contains technology-specific errors (that can be mistaken for biological signals), and that might need to be analyzed without access to a reference genome. Through the use of locality sensitive hashing methods, both the efficient estimation of sequence similarity and tolerance against the errors specific to biological data can be achieved. We developed a variant of the winnowing algorithm for local minimizer computation, which is specifically geared to deal with repetitive regions within biological sequences. Through compressing redundant information, we can both reduce the size of the hash tables required to save minimizer sketches, as well as reduce the amount of redundant low quality alignment candidates. Analyzing the distribution of segment lengths generated by this approach, we can better judge the size of required data structures, as well as identify hash functions feasible for this technique. Our evaluation could verify that simple and fast hash functions, even when using small hash value spaces (hash functions with small codomain), are sufficient to compute compressed minimizers and perform comparable to uniformly randomly chosen hash values. We also outlined an index for a taxonomic protein database using multiple compressed winnowings to identify alignment candidates. To store MinHash values, we present a cache-optimized implementation of a hash table using Hopscotch hashing to resolve collisions. As a biological application of similarity based analysis, we describe the analysis of double digest restriction site associated DNA sequencing (ddRADseq). We implemented a simulation software able to model the biological and technological influences of this technology to allow better development and testing of ddRADseq analysis software. Using datasets generated by our software, as well as data obtained from population genetic experiments, we developed an analysis workflow for ddRADseq data, based on the Stacks software. Since the quality of results generated by Stacks strongly depends on how well the used parameters are adapted to the specific dataset, we developed a Snakemake workflow that automates preprocessing tasks while also allowing the automatic exploration of different parameter sets. As part of this workflow, we developed a PCR deduplication approach able to generate consensus reads incorporating the base quality values (as reported by the sequencing device), without performing an alignment first. As an outlook, we outline a MinHashing approach that can be used for a faster and more robust clustering, while addressing incomplete digestion and null alleles, two effects specific for ddRADseq that current analysis tools cannot reliably detect

Semantic Models for Machine Learning

In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables. CCA can be seen as using complex labels as a way of guiding feature selection towards the underlying semantics. CCA makes use of two views of the same semantic object to extract a representation of the semantics. Secondly Partial Least Squares (PLS), a method similar to CCA. It selects feature directions that are useful for the task at hand, though PLS only uses one view of an object and the label as the corresponding pair. PLS could be thought of as a method that looks for directions that are good for distinguishing the different labels. The third method is the Fisher kernel. A method that aims to extract more information of a generative model than simply by their output probabilities. The aim is to analyse how the Fisher score depends on the model and which aspects of the model are important in determining the Fisher score. We focus our theoretical investigation primarily on CCA and its kernel variant. Providing a theoretical analysis of the method's stability using Rademacher complexity, hence deriving the error bound for new data. We conclude the thesis by applying the described approaches to problems in the various fields of image, text, music application and medical analysis, describing several novel applications on relevant real-world data. The aim of the thesis is to provide a theoretical understanding of semantic models, while also providing a good application foundation on how these models can be practically used

Sounds of Science: Copyright Infringement in AI Music Generator Outputs

The music business is no stranger to disruptive technology. The industry’s apparent comeback from the devastating downturn caused by illegal file sharing seems to have arrived just in time for what may be an even more disruptive technological phenomenon: artificial intelligence (“AI”). Much has been said about the implications of AI-generated music, ranging from issues of ownership, to rights of publicity. However, there has been surprisingly little discussion of infringement in the AI systems’ outputs. By examining the functionality of AI music generators through the lens of de minimis use case law, this paper will explain how the outputs of AI music generators potentially infringe the exclusive reproduction right granted to musical work and sound recording copyright owners. Going forward, courts and policymakers must not ignore AI’s capacity to undermine our incentives for human authorship, and craft rules that promote a mutually beneficial AI music ecosystem for technology companies and copyright owners alike

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.Tämä tutkimus esittelee uuden musiikkianalyyttisen metodin, vertailurakenneanalyysin (VRA, engl. Comparison Structure Analysis, CSA), jonka avulla voidaan analysoida musiikin eri ulottuvuuksia, kuten harmoniaa tai rytmiä. VRA:n ideana on mitata tietyn ennalta valitun musiikillisen rakenteen, vaikkapa jonkin sävelasteikon, vallitsevuutta musiikin kullakin ajanhetkellä. Tämä edellyttää kolmea asiaa. Ensiksi, intuitiivisesti tai muulla tavoin valittu musiikillinen piirre, jota tässä kutsutaan yleisesti vertailurakenteeksi, on esitettävä matemaattisessa muodossa, esimerkiksi matemaattisen avaruuden vektorina. Vertailurakenne voidaan muodostaa myös useiden eri tyyppisten, musiikin eri ulottuvuuksiin liittyvien tietorakenteiden yhdistelmänä. Toiseksi, analysoitava musiikillinen data, esimerkiksi musiikista muodostetut sävelluokat (C:stä H:hon), on pystyttävä ryhmittelemään vastaavantyyppisiksi objekteiksi. Lisäksi tarvitaan vielä matemaattinen funktio, joka kykenee mittaamaan valitun vertailurakenteen ja musiikista ryhmiteltyjen segmenttien välistä samankaltaisuutta tai vastaavasti, etäisyyttä. Toisin sanoen, VRA:ssa verrataan valittua vertailurakennetta, esimerkiksi diatonista asteikkoa, kaikkiin musiikista segmentoituihin vastaavantyyppisiin objekteihin. Mittaustulokset saadaan lukuarvoina yleensä välillä 0–1, jossa arvo 1 voi – mittausfunktion luonteesta riippuen – tarkoittaa joko täydellistä samankaltaisuutta tai suurinta mahdollista etäisyyttä. Havainnollisena analyysin kohteena voisimme kuvitella länsimaista taidemusiikkia edustavan sävellyksen, jossa siirrytään keskiaikaisesta diatonisesta musiikista historiallisesti ja tyylillisesti kohti 1900-luvun atonaalista musiikkia. Mikäli tässä tapauksessa vertailurakenteena käytettäisiin mainittua diatonista asteikkoa, VRA paljastaisi musiikissa korvinkin havaittavan ei-diatonisoitumisen. Tulosten esittämisellä esimerkiksi ajallisia muutoksia esittävin mittauskäyrin tai luokittelua havainnollistavin keskiarvopistein on merkittävä asema analyysissa. VRA sijoittuu perinteisen musiikkianalyysin ja tietokonetta hyödyntävien musiikin sisältöhakuun (music information retrieval, MIR) keskittyvien tekniikoiden välimaastoon. Sen avulla voidaan tunnistaa ja mitata perinteiselle musiikkianalyysille tyypillisia kohteita kuten karakteristisia rytmejä, sävelluokkajoukkoja, joukkoluokkia, tonaliteetteja ja käänteiskontrapunkteja soveltamalla MIR:lle tyypillisiä segmentointi- ja vertailualgoritmeja. Vertailurakenneanalyysin suurimmaksi haasteeksi on osoittautunut musiikillisten segmenttien muodostamiseen tarvittavan automaattisen algoritmin kehittäminen. Voidaan näet osoittaa, että sama musiikillinen data on useimmiten mahdollista segmentoida – musiikillisesti mielekkäästi – monella eri tavalla. Silloin, kun kyse on harmoniaan liittyvistä objekteista, tehtävä on erityisen haastava, sillä tällöin musiikin säveltapahtumia joudutaan tarkastelemaan niin ajallisessa kuin vertikaalisessakin suunnassa. Musiikin tonaalisuudessa ja sävelluokkasisällössä tapahtuvien muutosten analysoimista varten tässä tutkimuksessa kehitettiinkin kaksi erilaista segmentointialgoritmia, jotka muodostavat musiikillisesta datasta osin limittäisiä sävelluokkajoukkoja. Metodien erilaisuudesta huolimatta ‘herkkyysanalyysillä’ voitiin osoittaa, että molemmat menetelmät ovat hyvin vähän riippuvaisia syötetyn datan luonteesta; niiden avulla saadut tulokset olivat hyvin samankaltaisia. VRA:lla saatuja tuloksia voidaan edelleen tarkastella myös tilastollisen merkitsevyyden näkökulmasta. Koska VRA:lla pystytään havaitsemaan musiikin eri dimensioissa tapahtuvia muutoksia, tämän johdannaisena voidaan tutkia myös sitä, missä määrin jokin sävellys on tyylillisesti koherentti verrattuna johonkin toiseen sävellykseen eli kummassa muutokset ovat tarkasteltavan ominaisuuden suhteen keskimäärin pienemmät ja kummassa suuremmat. Lisäksi VRA tarjoaa mahdollisuuden musiikin luokitteluun saatujen mittausarvojen perusteella: mitä enemmän musiikillisia parametrejä ja useampia vertailurakenteita analyysissa hyödynnetään, sitä tarkemmin sävellyksiä voidaan luokitella. Niinpä VRA:n keinoja voidaan tulevaisuudessa kuvitella käytettävän myös musiikin sisältöhakuun (MIR). Tällaisessa tapauksessa vertailurakenne tai -rakenteet voitaisiin ‘laskea’ musiikillisesta datasta suoraan jollakin matemaattisella menetelmällä – kuten pääkomponenttianalyysilla – etukäteen suoritettavan intuitiivisen valinnan sijaan. Tutkimuksen tuloksiin lukeutuvat myös useat VRA:n tarpeisiin kehitetyt samankaltaisuusmittarit. Näistä mielenkiintoisin lienee sävelluokkajoukkojen välisen samankaltaisuuden mittaamiseen kehitetty funktio expcos, joka löytyi ns. geneettisen ohjelmoinnin avulla. Mainitussa kokeessa tietokoneella generoitiin arviolta n. 800 000 samankaltaisuusmittaria, joiden tuottamia tuloksia verrattiin ihmisten tekemiin samankaltaisuusarvioihin. Niistä n. 450 osoittautui käyttökelpoiseksi. Sensitiivisyysanalyysi osoitti, että em. funktio paitsi korreloi voimakkaammin empiiristen samankaltaisuusarvioiden kanssa, on VRA:ssa myös robustimpi kuin kenties tunnetuin samaan tarkoitukseen kehitetty funktio, REL (David Lewin, 1980). Käytännössä tällä ei ole kuitenkaan merkitystä: REL toimii VRA:ssa aivan yhtä hyvin kuin expcos. VRA:n avulla musiikkia tarkastellaan ikään kuin jonkinlaisena tilastollisena sävelmassana, eikä se niin muodoin kykene kertomaan siitä, miten analysoitava musiikki on yksityiskohtien tasolla sävelletty; perinteiset musiikkianalyysimenetelmät pureutuvat tehtävään paremmin. Toisaalta, tämä ei ole VRA:n tarkoituskaan vaan päinvastoin, sen avulla sävellysten muodosta pystytään muodostamaan laajoja yleiskuvia, jotka ovat useimmiten havaintokykymme ulottumattomissa. Vertailurakenneanalyysi on hyvin joustava menetelmä. Mikään ei nimittäin estä tarkastelemasta musiikin eri dimensioista saatuja mittaustuloksia keskenään ja näin etsimästä niiden välisiä yhteyksiä. Lisäksi menetelmän periaatteita voitaisiin kuvitella käytettävän yleisemminkin, esimerkiksi linnunlaulun muodon tarkasteluun tai vaikkapa jokipuron solinasta löytyvien toistuvien jaksojen havainnointiin. VRA:n periaatteita voidaankin soveltaa mihin tahansa numeerisesti diskreettiin muotoon saatettuun aikasarjaan.Siirretty Doriast

Surface detection of alkaline ultramafic rocks in semi-arid and arid terrains using spectral geological techniques

Studies have been completed into the spectral response of alkaline and other ultramafic rocks from arid and semi-arid regions. To date these rocks have not been investigated spectrally in a systematic fashion using the latest field and airborne imaging spectrometers. The objective was to determine how spectral geological techniques could be used to locate these rock types when they are exposed, weathered and reduced to residual soil. The data used in this study were spectra obtained from field spectrometers (PIMA and GER IRIS MkIV) and airborne scanners (GEOSCAN MkII, GER IS and HyMap). These data were gathered at four sites within Australia. The data processing software packages used for the analyses were commercially available image processing systems (is S'600and ENVI) and a modified version of PIMAVIEW for processing spectra. Spectra were measured, in the field and the laboratory, of alkaline and other ultramafic rocks to determine if they had diagnostic spectral absorption features. These studies demonstrated that there are diagnostic spectral absorption features common to alkaline and other ultramafic rocks including dunite, peridotite and serpentinite. The diagnostic spectrum in the SWIR2 region (2000nm to 2500nm) has absorption features located near 2300nm and 2380nm and results from Mg-OH bearing minerals including serpentine, talc and phlogopite. The VNIR spectra of these rocks also have distinct absorption features, but since these are caused by minerals that are common to a variety of rocks and soils, they have not been considered in this study. When weathered under arid and semi-arid conditions, ultramafic rocks break down into smectite clays, primarily saponite. Saponite has a similar spectral signature to the primary Mg-OH bearing minerals. Further weathering and removal of magnesium results in saponite altering to kaolinite and then to opaline silica. These minerals may occur in residual soils derived from ultramafic rocks. Spectral investigation of mixtures of saponite and other minerals showed that there are linear changes the absorption features of spectra, depth, wavelength and shape, as the proportion of saponite to other minerals varies. The ability to identify ultramafic rocks from the distribution of their diagnostic spectra was confirmed by analysing data obtained from surface samples and airborne scanners. The coincidence of results obtained from field studies and airborne scanner data signifies that data acquired remotely are as useful as spectra obtained in the field, for mapping the extent of ultramafic rocks. Present and past imaging systems have been investigated to determine the specifications required to suit this application. These investigations included sampling the spectra of ultramafic and background rocks with the band pass characteristics of various instruments. The signal-to-noise ratio that is required of scanner data to ensure it is useable was also studied. These studies indicated a specification for data of at least eight and preferably, thirty two channels in the SWIR2 (between 2000nm and 2500nm) obtained with a signalto- noise ratio in excess of 200: 1, ideally 400: 1 at 2200nm. Scanners producing data with these specifications can be used to locate ultramafic rocks from their spectral signature, whether exposed or covered by residual soils in a variety of geological environments. The success of applying these techniques will however depend on the spectral contrast between ultramafic rocks and their backgrounds. Rocks that produce Mg-OH bearing minerals or have near 2300nm absorption can result in areas being mistakenly identified as containing alkaline and other ultramafic rocks. However, in this study it was demonstrated that carbonate soils and rocks that have a near 2300nm absorption feature can be spectrally distinguished from alkaline ultramafic derived saponite using HyMap scanner data. In South Australia dolomite appears to have altered to saponite at surface but kimberlites in the area can still be detected by processing HyMap scanner data with spectral as opposed to conventional image processing techniques

Population Structure and Frankish Ethnogenesis (AD 400-900)

abstract: The transition from Late Antiquity to Early Medieval Europe (ca. AD 400-900) is often characterized as a period of ethnogenesis for a number of peoples, such as the Franks. Arising during protracted contact with the Roman Empire, the Franks would eventually form an enduring kingdom in Western Europe. However, there is little consensus about the processes by which they formed an ethnic group. This study takes a fresh look at the question of Frankish ethnogenesis by employing a number of theoretical and methodological subdisciplines, including population genetics and ethnogenetic theory. The goals of this work were 1) to validate the continued use of biological data in questions of historical and archaeological significance; and 2) to elucidate how Frankish population structure changed over time. Toward this end, measurements from the human dentition and crania were subjected to rigorous analytical techniques and interpreted within a theoretical framework of ethnogenetic life cycles. Results validate existing interpretations of intra-regional biological continuity over time. However, they also reveal that 1) there are clear biological and geographical differences between communities, and 2) there are hints of diachronic shifts, whereby some communities became more similar to each other over time. These conclusions complement current ethnohistoric work arguing for the increasing struggle of the Frankish kingdom to unify itself when confronted by strong regionally-based politics.Dissertation/ThesisDoctoral Dissertation Anthropology 201