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Relevant bibliographies by topics / Music information retrieval / Dissertations / Theses

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Dissertations / Theses on the topic 'Music information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Koenders, Michael. "FROM MUSIC INFORMATION RETRIEVAL (MIR) TO INFORMATION RETRIEVAL FOR MUSIC (IRM)." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16914.

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This thesis reviews and discusses certain techniques from the domain of (Music) Information Retrieval, in particular some general data mining algorithms. It also describes their specific adaptations for use as building blocks in the CACE4 software application. The use of Augmented Transition Networks (ATN) from the field of (Music) Information Retrieval is, to a certain extent, adequate as long as one keeps the underlying tonal constraints and rules as a guide to understanding the structure one is looking for. However since a large proportion of algorithmic music, including music composed by t

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2

Shao, Bo. "User-centric Music Information Retrieval." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/416.

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The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for

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3

Boland, Daniel. "Engaging with music retrieval." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6727/.

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Music collections available to listeners have grown at a dramatic pace, now spanning tens of millions of tracks. Interacting with a music retrieval system can thus be overwhelming, with users offered ‘too-much-choice’. The level of engagement required for such retrieval interactions can be inappropriate, such as in mobile or multitasking contexts. Music recommender systems are widely employed to address this issue, however tend toward the opposite extreme of disempowering users and suffer from issues of subjectivity and confounds, such as the equalisation of tracks. This challenge and the styl

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4

Page, Stephen Dowland. "Computer tools for music information retrieval." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293399.

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Pérez-Sancho, Carlos. "Stochastic language models for music information retrieval." Doctoral thesis, Universidad de Alicante, 2009. http://hdl.handle.net/10045/14217.

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6

Pearce, Alastair T. P. "MAP : a computer program for music information retrieval." Thesis, King's College London (University of London), 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264938.

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7

Martinek, Václav. "Rozpoznávání hudebních coververzí pomocí technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442587.

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This master’s thesis deals with designs and implementation of systems for music cover recognition. The introduction part is devoted to the calculation parameters from audio signal using Music Information Retrieval techniques. Subsequently, various forms of cover versions and musical aspects that cover versions share are defined. The thesis also deals in detail with the creation and distribution of a database of cover versions. Furthermore, the work presents methods and techniques for comparing and processing the calculated parameters. Attention is then paid to the OTI method, CSM calculation a

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Zemánková, Šárka. "Rozpoznávání hudebního žánru za pomoci technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401988.

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This diploma work deals with music genre recognition using the techniques of Music Information Retrieval. It contains a brief description of the principle of this research area and its subfield called Music Genre Recognition. The following chapter includes selection of the most suitable parameters for describing music genres. This work further characterizes machine learning methods used in this field of research. The next chapter deals with the descriptions of music datasets created for genre classification studies. Subsequently, there is a draft and evaluation of the system for music genre re

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Deng, Jie. "Emotion-based music retrieval and recommendation." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/82.

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The digital music industry has expanded dramatically during the past decades, which results in the generation of enormous amounts of music data. Along with the Internet, the growing volume of quantitative data about users (e.g., users’ behaviors and preferences) can be easily collected nowadays. All these factors have the potential to produce big data in the music industry. By utilizing big data analysis of music related data, music can be better semantically understood (e.g., genres and emotions), and the user’s high-level needs such as automatic recognition and annotation can be satisfied. F

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10

Reed, Jeremy T. "Acoustic segment modeling and preference ranking for music information retrieval." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37189.

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This dissertation focuses on improving content-based recommendation systems for music. Specifically, progress in the development in music content-based recommendation systems has stalled in recent years due to some faulty assumptions: 1. most acoustic content-based systems for music information retrieval (MIR) assume a bag-of-frames model, where it is assumed that a song contains a simplistic, global audio texture 2. genre, style, mood, and authors are appropriate categories for machine-oriented recommendation 3. similarity is a universal construct and does not vary among different users The

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Hitland, Håkon Haugdal. "Using the Signature Quadratic Form Distance for Music Information Retrieval." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14472.

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This thesis is an investigation into how the signature quadratic form distance can be used to search in music.Using the method used for images by Beecks, Uysal and Seidl as a starting point,I create feature signatures from sound clips by clustering features from their frequency representations.I compare three different feature types, based on Fourier coefficients, mel frequency cepstrum coefficients (MFCCs), and the chromatic scale.Two search applications are considered.First, an audio fingerprinting system, where a music file is located by a short recorded clip from the song.I run experiments

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Paradzinets, Aliaksandr V. "Variable resolution transform-based music feature extraction and their applications for music information retrieval." Ecully, Ecole centrale de Lyon, 2007. http://www.theses.fr/2007ECDL0047.

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Dans le secteur de loisirs il y a un nombre considérable d’enregistrements numériques musicaux produits, diffusés et échangés qui favorise la demande croisante de services intelligents de recherche de musique. La navigation par contenu devient cruciale pour permettre aux professionnels et également aux amateurs d’accéder facilement aux quantités de données musicales disponibles. Ce travail présente les nouveaux descripteurs de contenu musical et mesures de similarité qui permettent l’organisation automatique de données musicales (recherche par similarité, génération automatique des playlistes)

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Smělý, Pavel. "Rozpoznávání hudební nálady a emocí za pomoci technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401982.

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This work focuses on scientific area called Music Information Retrieval, more precisely it’s subdivision focusing on the recognition of emotions in music called Music Emotion Recognition. The beginning of the work deals with general overview and definition of MER, categorization of individual methods and offers a comprehensive view of this discipline. The thesis also concentrates on the selection and description of suitable parameters for the recognition of emotions, using tools openSMILE and MIRtoolbox. A freely available DEAM database was used to obtain the set of music recordings and their

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McLeod, Andrew Philip. "Language of music : a computational model of music interpretation." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31371.

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Automatic music transcription (AMT) is commonly defined as the process of converting an acoustic musical signal into some form of musical notation, and can be split into two separate phases: (1) multi-pitch detection, the conversion of an audio signal into a time-frequency representation similar to a MIDI file; and (2) converting from this time-frequency representation into a musical score. A substantial amount of AMT research in recent years has concentrated on multi-pitch detection, and yet, in the case of the transcription of polyphonic music, there has been little progress. There are many

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Simon, Scott J. "A multi-dimensional entropy model of jazz improvisation for music information retrieval." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4929/.

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Jazz improvisation provides a case context for examining information in music; entropy provides a means for representing music for retrieval. Entropy measures are shown to distinguish between different improvisations on the same theme, thus demonstrating their potential for representing jazz information for analysis and retrieval. The calculated entropy measures are calibrated against human representation by means of a case study of an advanced jazz improvisation course, in which synonyms for "entropy" are frequently used by the instructor. The data sets are examined for insights in music info

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Suyoto, Iman S. H., and ishs@ishs net. "Cross-Domain Content-Based Retrieval of Audio Music through Transcription." RMIT University. Computer Science and Information Technology, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090527.092841.

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Research in the field of music information retrieval (MIR) is concerned with methods to effectively retrieve a piece of music based on a user's query. An important goal in MIR research is the ability to successfully retrieve music stored as recorded audio using note-based queries. In this work, we consider the searching of musical audio using symbolic queries. We first examined the effectiveness of using a relative pitch approach to represent queries and pieces. Our experimental results revealed that this technique, while effective, is optimal when the whole tune is used as a query. We th

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Persaud, Felicia Nafeeza. "In Search of Computer Music Analysis: Music Information Retrieval, Optimization, and Machine Learning from 2000-2016." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38007.

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My thesis aims to critically examine three methods in the current state of Computer Music Analysis. I will concentrate on Music Information Retrieval, Optimization, and Machine Learning. My goal is to describe and critically analyze each method, then examine the intersection of all three. I will start by looking at David Temperley’s The Cognition of Basic Musical Structures (2001) which offers an outline of major accomplishments before the turn of the 21st century. This outline will provide a method of organization for a large portion of the thesis. I will conclude by explaining the most rece

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Cloete, Candice Lynn. "MIRMaid : an interface for a content based Music Information Retrieval test-bed." Thesis, University of Cape Town, 2006. http://pubs.cs.uct.ac.za/archive/00000460/.

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Music Information Retrieval (MlR) is the interdisciplinary science of retrieving information from music and includes influences from different areas, like music perception and cognition, music analysis, signal processing, music indexing and information retrieval [Futrelle & Downie, 2003]. To produce the most efficient MlR systems, test-beds are commonly used to test different combinations of parameters against each other. The purpose of this dissertation was to investigate the composition of algorithms for MlR systems by constructing an interface that could form part of a test-bed. It differs

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Kárník, Radoslav. "Rozpoznávání hudebních nástrojů ze zvukových nahrávek za pomoci technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401984.

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This paper discusses design and implementation of classifying system for recognition of musical instruments from audio records with use of Musical Information Retrieval techniques. In the first part, paper describes parameters used for instrument classification, calculation of said parameters from records and reduction of feature vector. Next part is devoted to tuning and implementation of various classifiers with focus on neural networks. These classifiers ar further tested on records from IRMAS dataset wchich contain 11 musical instruments playing solo or with other instruments. Results of c

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Jelínková, Jana. "Rozpoznání hudebního slohu z orchestrální nahrávky za pomoci technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413256.

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As all genres of popular music, classical music consists of many different subgenres. The aim of this work is to recognize those subgenres from orchestral recordings. It is focused on the time period from the very end of 16th century to the beginning of 20th century, which means that Baroque era, Classical era and Romantic era are researched. The Music Information Retrieval (MIR) method was used to classify chosen subgenres. In the first phase of MIR method, parameters were extracted from musical recordings and were evaluated. Only the best parameters were used as input data for machine learni

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Guichaoua, Corentin. "Modèles de compression et critères de complexité pour la description et l'inférence de structure musicale." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S053/document.

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Une définition très générale de la structure musicale consiste à considérer tout ce qui distingue la musique d'un bruit aléatoire comme faisant partie de sa structure. Dans cette thèse, nous nous intéressons à l'aspect macroscopique de cette structure, en particulier la décomposition de passages musicaux en unités autonomes (typiquement, des sections) et à leur caractérisation en termes de groupements d'entités élémentaires conjointement compressibles. Un postulat de ce travail est d'établir un lien entre l'inférence de structure musicale et les concepts de complexité et d'entropie issus de la

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O'Brien, Cian John. "Supervised feature learning via sparse coding for music information rerieval." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53615.

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This thesis explores the ideas of feature learning and sparse coding for Music Information Retrieval (MIR). Sparse coding is an algorithm which aims to learn new feature representations from data automatically. In contrast to previous work which uses sparse coding in an MIR context the concept of supervised sparse coding is also investigated, which makes use of the ground-truth labels explicitly during the learning process. Here sparse coding and supervised coding are applied to two MIR problems: classification of musical genre and recognition of the emotional content of music. A variation of

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Kitahara, Tetsuro. "Computational musical instrument recognition and its application to content-based music information retrieval." 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/135955.

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Hargreaves, Steven. "Music metadata capture in the studio from audio and symbolic data." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8816.

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Music Information Retrieval (MIR) tasks, in the main, are concerned with the accurate generation of one of a number of different types of music metadata {beat onsets, or melody extraction, for example. Almost always, they operate on fully mixed digital audio recordings. Commonly, this means that a large amount of signal processing effort is directed towards the isolation, and then identification, of certain highly relevant aspects of the audio mix. In some cases, results of one MIR algorithm are useful, if not essential, to the operation of another { a chord detection algorithm for example, is

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Tanur, Luke. "A Geometric Approach to Pattern Matching in Polyphonic Music." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1132.

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The music pattern matching problem involves finding matches of a small fragment of music called the "pattern" into a larger body of music called the "score". We represent music as a series of horizontal line segments in the plane, and reformulate the problem as finding the best translation of a small set of horizontal line segments into a larger set of horizontal line segments. We present an efficient algorithm that can handle general weight models that measure the musical quality of a match of the pattern into the score, allowing for approximate pattern matching. We give an algorith

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Downie, John Stephen. "Evaluating a simple approach to music information retrieval, conceiving melodic n-grams as text." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0008/NQ42513.pdf.

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Miotto, Riccardo. "Content-based Music Access: Combining Audio Features and Semantic Information for Music Search Engines." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421582.

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During the last decade, the Internet has reinvented the music industry. Physical media have evolved towards online products and services. As a consequence of this transition, online music corpora have reached a massive scale and are constantly being enriched with new documents. At the same time, a great quantity of cultural heritage content remains undisclosed because of the lack of metadata to describe and contextualize it. This has created a need for music retrieval and discovery technologies that allow users to interact with all these music repositories efficiently and effectively. Musi

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Boenn, Georg. "Automated analysis and transcription of rhythm data and their use for composition." Thesis, University of Bath, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538138.

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Miklánek, Štěpán. "Určení místa původu hudebních interpretací české komorní a orchestrální hudby za pomoci technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401985.

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This diploma thesis is focused on the statistical analysis of chamber and orchestral classical music recordings composed by Czech authors. One of the chapters is dedicated to the description of a feature extraction process that precedes the statistical analysis. Techniques of Music Information Retrieval are used during several stages of this thesis. Databases used for analysis are described and pre-processing steps are proposed. A tool for synchronization of the recordings was implemented in MATLAB. Finally the system used for classification of recordings based on their geographical origin is

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Valero-Mas, Jose J. "Towards Interactive Multimodal Music Transcription." Doctoral thesis, Universidad de Alicante, 2017. http://hdl.handle.net/10045/71275.

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La transcripción de música por computador es de vital importancia en tareas del llamo campo de la Extracción y recuperación de información musical por su utilidad como proceso para la obtención de una abstracción simbólica que codifica el contenido musical de un fichero de audio. En esta disertación se estudia este problema desde una perspectiva diferente a la típicamente considerada para estos problemas, la perspectiva interactiva y multimodal. En este paradigma el usuario cobra especial importancia puesto que es parte activa en la resolución del problema (interactividad); por otro lado, la m

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Panteli, Maria. "Computational analysis of world music corpora." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/36696.

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The comparison of world music cultures has been considered in musicological research since the end of the 19th century. Traditional methods from the field of comparative musicology typically involve the process of manual music annotation. While this provides expert knowledge, the manual input is timeconsuming and limits the potential for large-scale research. This thesis considers computational methods for the analysis and comparison of world music cultures. In particular, Music Information Retrieval (MIR) tools are developed for processing sound recordings, and data mining methods are conside

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Choi, Keunwoo. "Deep neural networks for music tagging." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/46029.

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In this thesis, I present my hypothesis, experiment results, and discussion that are related to various aspects of deep neural networks for music tagging. Music tagging is a task to automatically predict the suitable semantic label when music is provided. Generally speaking, the input of music tagging systems can be any entity that constitutes music, e.g., audio content, lyrics, or metadata, but only the audio content is considered in this thesis. My hypothesis is that we can fi nd effective deep learning practices for the task of music tagging task that improves the classi fication performanc

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Knopke, Ian. "Building a search engine for music and audio on the World Wide Web." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85177.

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The main contribution of this dissertation is a system for locating and indexing audio files on the World Wide Web. The idea behind this system is that the use of both web page and audio file analysis techniques can produce more relevant information for locating audio files on the web than is used in full-text search engines.<br>The most important part of this system is a web crawler that finds materials by following hyperlinks between web pages. The crawler is distributed and operates using multiple computers across a network, storing results to a database. There are two main component

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Sephus, Nashlie H. "A framework for exploiting modulation spectral features in music data mining and other applications." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52243.

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When a signal is decomposed into frequency bands, demodulated into modulator and carrier pairs, and portrayed in a carrier frequency-versus modulator frequency domain, significant information may be automatically observed about the signal. We refer to this domain as the modulation spectral domain. The modulation spectrum is referred to as a windowed Fourier transform across time that produces an acoustic frequency versus modulation frequency representation of a signal. Previously, frameworks incorporating the discrete short-time modulation transform (DSTMT) and modulation spectrum have been

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Klausing, Tilo. "Robuste Genre-Klassifikation." Universitätsbibliothek Chemnitz, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200800184.

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Die automatische Klassiﬁkation von Musik in Genres wird seit einigen Jahren systematisch erforscht. In dieser Zeit wurden Genre-Klassiﬁkationssysteme und ihre Komponenten immer weiter verbessert, wofür die verschiedensten Richtungen eingeschlagen wurden. Diese Arbeit gibt deshalb im ersten Teil einen umfassenden Überblick über das Forschungsgebiet der Genre-Klassiﬁkation, von den grundlegenden Techniken bis zum aktuellen Forschungsstand. Im zweiten Teil der Arbeit wird ein neuartiger Ansatz vorgestellt, der das Ziel hat, die Genre-Klassiﬁkation gegen eventuelle Störungen robuster zu machen. Di

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McEnnis, Daniel. "On-demand metadata extraction network (OMEN)." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99382.

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OMEN (On-demand Metadata Extraction Network) addresses a fundamental problem in Music Information Retrieval: the lack of universal access to a large dataset containing significant amounts of copyrighted music. This thesis proposes a solution to this problem that is accomplished by utilizing the large collections of digitized music available at many libraries. Using OMEN, libraries will be able to perform on-demand feature extraction on site, returning feature values to researchers instead of providing direct access to the recordings themselves. This avoids copyright difficulties, since the und

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Wilmering, Thomas. "Applications of Semantic Web technologies in music production." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9078.

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The development of tools and services for the realisation of the Semantic Web has been a very active field of research in recent years, with a strong focus on linking existing data. In the field of music information management, Semantic Web technologies may facilitate searching and browsing, and help to reveal relationships with data from other domains. At the same time, many algorithms have been developed to extract low and high-level features, which enable the user to analyse music and audio in detail. The use of semantics in the process of music production however is still a relatively new

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Lee, Kyogu. "A system for acoustic chord transcription and key extraction from audio using hidden Markov models trained on synthesized audio /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Al-Shakarchi, Ahmad. "Scalable audio processing across heterogeneous distributed resources : an investigation into distributed audio processing for Music Information Retrieval." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/47855/.

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Audio analysis algorithms and frameworks for Music Information Retrieval (MIR) are expanding rapidly, providing new ways to discover non-trivial information from audio sources, beyond that which can be ascertained from unreliable metadata such as ID3 tags. MIR is a broad field and many aspects of the algorithms and analysis components that are used are more accurate given a larger dataset for analysis, and often require extensive computational resources. This thesis investigates if, through the use of modern distributed computing techniques, it is possible to design an MIR system that is scala

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Smith, Jordan. "Explaining listener differences in the perception of musical structure." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/27225.

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State-of-the-art models for the perception of grouping structure in music do not attempt to account for disagreements among listeners. But understanding these disagreements, sometimes regarded as noise in psychological studies, may be essential to fully understanding how listeners perceive grouping structure. Over the course of four studies in different disciplines, this thesis develops and presents evidence to support the hypothesis that attention is a key factor in accounting for listeners' perceptions of boundaries and groupings, and hence a key to explaining their disagreements. First, we

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Masada, Kristen S. "Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music." Ohio University Honors Tutorial College / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1524847168750137.

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Sanden, Christopher, and University of Lethbridge Faculty of Arts and Science. "An empirical evaluation of computational and perceptual multi-label genre classification on music / Christopher Sanden." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2602.

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Automatic music genre classi cation is a high-level task in the eld of Music Information Retrieval (MIR). It refers to the process of automatically assigning genre labels to music for various tasks, including, but not limited to categorization, organization and browsing. This is a topic which has seen an increase in interest recently as one of the cornerstones of MIR. However, due to the subjective and ambiguous nature of music, traditional single-label classi cation is inadequate. In this thesis, we study multi-label music genre classi cation from perceptual and computational perspectives. F

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Fiebrink, Rebecca. "An exploration of feature selection as a tool for optimizing musical genre classification /." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99372.

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The computer classification of musical audio can form the basis for systems that allow new ways of interacting with digital music collections. Existing music classification systems suffer, however, from inaccuracy as well as poor scalability. Feature selection is a machine-learning tool that can potentially improve both accuracy and scalability of classification. Unfortunately, there is no consensus on which feature selection algorithms are most appropriate or on how to evaluate the effectiveness of feature selection. Based on relevant literature in music information retrieval (MIR) and machin

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Bernardo, Alexandre. "Features for the Classification and Clustering of Music in Symbolic Format." Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/13947.

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Music Information Retrieval is, nowadays, a highly active branch of research and development in the computer science field, and focuses several topics, including music genre classification. The work presented in this paper focus on Track and Genre Classification of music stored using MIDI format, To address the problem of MIDI track classification, we extract a set of descriptors that are used to train a classifier implemented by a Neural Network, based on the pitch levels and durations that describe each track. Tracks are classified into four classes: Melody, Harmony, Bass and Drums. In order

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Gängler, Thomas. "Semantic Federation of Musical and Music-Related Information for Establishing a Personal Music Knowledge Base." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-72434.

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Music is perceived and described very subjectively by every individual. Nowadays, people often get lost in their steadily growing, multi-placed, digital music collection. Existing music player and management applications get in trouble when dealing with poor metadata that is predominant in personal music collections. There are several music information services available that assist users by providing tools for precisely organising their music collection, or for presenting them new insights into their own music library and listening habits. However, it is still not the case that music consumer

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Craft, Alastair. "The role of culture in music information retrieval : a model of negotiated musical meaning, and its implications in methodology and evaluation of the music genre classification task." Thesis, Goldsmiths College (University of London), 2008. http://research.gold.ac.uk/6660/.

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This thesis proposes a new methodology for evaluation of automatic music genre classification. It is argued that the common tacit understanding that genre is an attribute of a piece is unsound, and that genre is better understood as an attribute given to a piece by a group of people. As a direct consequence of this, different groups will assign different genre labels to the same piece.

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Sordo, Mohamed. "Semantic annotation of music collections: A computational approach." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/79132.

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El consum de la música ha canviat dràsticament en els últims anys. Amb l’arribada de la música digital, el cost de producció s’ha reduït considerablement. L’expansió de la Web ha ajudat a promoure l’exploració de molt més contingut musical. Algunes botigues musicals on-line, com iTunes o Amazon, posseeixen milions de cançons a les seves col.leccions. No obstant, accedir a aquestes col.leccions d’una manera eficient és encara un gran repte. En aquesta tesis ens centrem en el problema d’anotar col.leccions musicals amb paraules semàntiques, també conegudes com tags. Els mètodes utilitzats

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Streich, Sebastian. "Music complexity: a multi-faceted description of audio content." Doctoral thesis, Universitat Pompeu Fabra, 2007. http://hdl.handle.net/10803/7545.

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Esta tesis propone un juego de algoritmos que puede emplearse para computar estimaciones de las distintas facetas de complejidad que ofrecen señales musicales auditivas. Están enfocados en los aspectos de acústica, ritmo, timbre y tonalidad. Así pues, la complejidad musical se entiende aquí en el nivel más basto del común acuerdo entre oyentes humanos. El objetivo es obtener juicios de complejidad mediante computación automática que resulten similares al punto de vista de un oyente ingenuo. La motivación de la presente investigación es la de mejorar la interacción humana con colecciones de mús

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Almeida, Hugo Ricardo da Costa. "Automatic cymbal classification." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/4923.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Informática<br>Most of the research on automatic music transcription is focused on the transcription of pitched instruments, like the guitar and the piano. Little attention has been given to unpitched instruments, such as the drum kit, which is a collection of unpitched instruments. Yet, over the last few years this type of instrument started to garner more attention, perhaps due to increasing popularity of the drum kit in the western music. There has b

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Senturk, Sertan. "Computational modeling of improvisation in Turkish folk music using Variable-Length Markov Models." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42761.

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The thesis describes a new database of uzun havas, a non-metered structured improvisation form in Turkish folk music, and a system, which uses Variable-Length Markov Models (VLMMs) to predict the melody in the uzun hava form. The database consists of 77 songs, encompassing 10849 notes, and it is used to train multiple viewpoints, where each event in a musical sequence are represented by parallel descriptors such as Durations and Notes. The thesis also introduces pitch-related viewpoints that are specifically aimed to model the unique melodic properties of makam music. The predictability of the

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