Relevant bibliographies by topics / Music information processing

Academic literature on the topic 'Music information processing'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Music information processing"

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Zhao, Tian, and Patricia K. Kuhl. "Music, speech, and temporal information processing." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1760. http://dx.doi.org/10.1121/1.5067789.

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Goto, Masataka, and Keiji Hirata. "Recent studies on music information processing." Acoustical Science and Technology 25, no. 6 (2004): 419–25. http://dx.doi.org/10.1250/ast.25.419.

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Tsuboi, Kuniharu. "Computer music and musical information processing." Journal of the Institute of Television Engineers of Japan 42, no. 1 (1988): 49–55. http://dx.doi.org/10.3169/itej1978.42.49.

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Katayose, Haruhiro. "The Dawn of Kansei Information Processing. Application of Kansei Information Processing. Music Performance." Journal of the Institute of Image Information and Television Engineers 52, no. 1 (1998): 53–55. http://dx.doi.org/10.3169/itej.52.53.

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Bugos, Jennifer, and Wendy Mostafa. "Musical Training Enhances Information Processing Speed." Bulletin of the Council for Research in Music Education, no. 187 (January 1, 2011): 7–18. http://dx.doi.org/10.2307/41162320.

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Abstract The purpose of this research is to examine the effects of music instruction on information processing speed. We examined music’s role on information processing speed in musicians (N = 14) and non-musicians (N = 16) using standardized neuropsychological measures, the Paced Auditory Serial Addition Task (PASAT) and the Trail Making Test (TMT). Results of a One Way ANOVA indicate significantly (p < .05) enhanced performance by musicians compared to non-musicians on the PASAT and TMT (Part A and B). These results suggest that musical training has the capacity to enhance processing speed of auditory and visual content. Implications for music educators stemming from these findings include the need for inclusion of rhythmic sight-reading exercises and improvisational activities to reinforce processing speed.
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FUKAYAMA, Satoru. "Music Information Processing for Visualization with Musical Notations." Journal of the Visualization Society of Japan 40, no. 158 (2020): 19–22. http://dx.doi.org/10.3154/jvs.40.158_19.

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Atherton, Ryan P., Quin M. Chrobak, Frances H. Rauscher, Aaron T. Karst, Matt D. Hanson, Steven W. Steinert, and Kyra L. Bowe. "Shared Processing of Language and Music." Experimental Psychology 65, no. 1 (January 2018): 40–48. http://dx.doi.org/10.1027/1618-3169/a000388.

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Abstract. The present study sought to explore whether musical information is processed by the phonological loop component of the working memory model of immediate memory. Original instantiations of this model primarily focused on the processing of linguistic information. However, the model was less clear about how acoustic information lacking phonological qualities is actively processed. Although previous research has generally supported shared processing of phonological and musical information, these studies were limited as a result of a number of methodological concerns (e.g., the use of simple tones as musical stimuli). In order to further investigate this issue, an auditory interference task was employed. Specifically, participants heard an initial stimulus (musical or linguistic) followed by an intervening stimulus (musical, linguistic, or silence) and were then asked to indicate whether a final test stimulus was the same as or different from the initial stimulus. Results indicated that mismatched interference conditions (i.e., musical – linguistic; linguistic – musical) resulted in greater interference than silence conditions, with matched interference conditions producing the greatest interference. Overall, these results suggest that processing of linguistic and musical information draws on at least some of the same cognitive resources.
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Rammsayer, Thomas, and Eckart Altenmüller. "Temporal Information Processing in Musicians and Nonmusicians." Music Perception 24, no. 1 (September 1, 2006): 37–48. http://dx.doi.org/10.1525/mp.2006.24.1.37.

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The present study was designed to examine the general notion that temporal information processing is more accurate in musicians than in nonmusicians. For this purpose, 36 academically trained musicians and 36 nonmusicians performed seven different auditory temporal tasks. Superior temporal acuity for musicians compared to nonmusicians was shown for auditory fusion, rhythm perception, and three temporal discrimination tasks. The two groups did not differ, however, in terms of their performance on two tasks of temporal generalization. Musicians’superior performance appeared to be limited to aspects of timing which are considered to be automatically and immediately derived from online perceptual processing of temporal information. Unlike immediate online processing of temporal information, temporal generalizations, which involve a reference memory of sorts, seemed not to be influenced by extensive music training.
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Achkar, Charbel El, and Talar Atechian. "MEI2JSON: a pre-processing music scores converter." International Journal of Intelligent Information and Database Systems 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijiids.2021.10040316.

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Achkar, Charbel El, and Talar Atéchian. "MEI2JSON: a pre-processing music scores converter." International Journal of Intelligent Information and Database Systems 15, no. 1 (2022): 57. http://dx.doi.org/10.1504/ijiids.2022.120130.

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Dissertations / Theses on the topic "Music information processing"

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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 scalable as the number of participants increases, which adheres to copyright laws and restrictions, whilst at the same time enabling access to a global database of music for MIR applications and research. A scalable platform for MIR analysis would be of benefit to the MIR and scientific community as a whole. A distributed MIR platform that encompasses the creation of MIR algorithms and workflows, their distribution, results collection and analysis, is presented in this thesis. The framework, called DART - Distributed Audio Retrieval using Triana - is designed to facilitate the submission of MIR algorithms and computational tasks against either remotely held music and audio content, or audio provided and distributed by the MIR researcher. Initially a detailed distributed DART architecture is presented, along with simulations to evaluate the validity and scalability of the architecture. The idea of a parameter sweep experiment to find the optimal parameters of the Sub-Harmonic Summation (SHS) algorithm is presented, in order to test the platform and use it to perform useful and real-world experiments that contribute new knowledge to the field. DART is tested on various pre-existing distributed computing platforms and the feasibility of creating a scalable infrastructure for workflow distribution is investigated throughout the thesis, along with the different workflow distribution platforms that could be integrated into the system. The DART parameter sweep experiments begin on a small scale, working up towards the goal of running experiments on thousands of nodes, in order to truly evaluate the scalability of the DART system. The result of this research is a functional and scalable distributed MIR research platform that is capable of performing real world MIR analysis, as demonstrated by the successful completion of several large scale SHS parameter sweep experiments across a variety of different input data - using various distribution methods - and through finding the optimal parameters of the implemented SHS algorithm. DART is shown to be highly adaptable both in terms of the distributed MIR analysis algorithm, as well as the distribution
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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 then suggested an algorithm involving the use of pitch classes in conjunction with the longest common subsequence algorithm between a query and target, also using the whole tune as a query. We also proposed an algorithm that works effectively when only a small part of a tune is used as a query. The algorithm makes use of a sliding window in addition to pitch classes and the longest common subsequence algorithm between a query and target. We examined the algorithm using queries based on the beginning, middle, and ending parts of pieces. We performed experiments on an audio collection and manually-constructed symbolic queries. Our experimental evaluation revealed that our techniques are highly effective, with most queries used in our experiments being able to retrieve a correct answer in the first rank position. In addition, we examined the effectiveness of duration-based features for improving retrieval effectiveness over the use of pitch only. We investigated note durations and inter-onset intervals. For this purpose, we used solely symbolic music so that we could focus on the core of the problem. A relative pitch approach alongside a relative duration representation were used in our experiments. Our experimental results showed that durations fail to significantly improve retrieval effectiveness, whereas inter-onset intervals significantly improve retrieval effectiveness.
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Byron, Timothy Patrick. "The processing of pitch and temporal information in relational memory for melodies." View thesis, 2008. http://handle.uws.edu.au:8081/1959.7/37492.

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Thesis (Ph.D.) -- University of Western Sydney, 2008.
A thesis submitted to the University of Western Sydney, College of Arts, School of Psychology, in fulfilment of the requirements for the degree of Doctor of Philosophy. Includes bibliographical references.
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Meinz, Elizabeth J. "Musical experience, musical knowledge and age effects on memory for music." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30881.

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Montecchio, Nicola. "Alignment and Identification of Multimedia Data: Application to Music and Gesture Processing." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422091.

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The overwhelming availability of large multimedia collections poses increasingly challenging research problems regarding the organization of, and access to data. A general consensus has been reached in the Information Retrieval community, asserting the need for tools that move past metadata-based techniques and exploit directly the information contained in the media. At the same time, interaction with content has evolved beyond the traditional passive enjoyment paradigm, bringing forth the demand for advanced control and manipulation options. The aim of this thesis is to investigate techniques for multimedia data alignment and identification. In particular, music audio streams and gesture-capture time series are considered. Special attention is given to the efficiency of the proposed approaches, namely the realtime applicability of alignment algorithms and the scalability of identification strategies. The concept of alignment refers to the identification and matching of corresponding substructures in related entities. The focus of this thesis is directed towards alignment of sequences with respect to a single dimension, aiming at the identification and matching of significant events in related time series. The alignment of audio recordings of music to their symbolic representations serves as a starting point to explore different methodologies based on statistical models. A unified model for the real time alignment of music audio streams to both symbolic scores and audio references is proposed. Its advantages are twofold: unlike most state-of-the-art systems, tempo is an explicit parameter within the stochastic framework; moreover, both alignment problems can be formulated within a common framework by exploiting a continuous representation of the reference content. A novel application of audio alignment techniques was found in the domain of studio recording productions, reducing the human effort spent in manual repetitive tasks. Gesture alignment is closely related to the domain of music alignment, as the artistic aims and engineering solutions of both areas largely overlap. Expressivity in gesture performance can be characterized by both the choice of a particular gesture and the way the gesture is executed. The former aspect involves a gesture recognition task, while the latter is addressed considering the time-evolution of features and the way these differ from pre-recorded templates. A model, closely related to the mentioned music alignment strategy, is proposed, capable of simultaneously recognizing a gesture among many templates and aligning it against the correct reference in realtime, while jointly estimating signal feature such as rotation, scaling, velocity. Due to the increasingly large volume of music collections, the organization of media items according to their perceptual characteristics has become of fundamental importance. In particular, content-based identification technologies provide the tools to retrieve and organize music documents. Music identification techniques should ideally be able to identify a recording -- by comparing it against a set of known recordings -- independently from the particular performance, even in case of significantly different arrangements and interpretations. Even though alignment techniques play a central role in many works of the music identification literature, the proposed methodology addresses the task using techniques that are usually associated to textual IR. Similarity computation is based on hashing, attempting at creating collisions between vectors that are close in the feature space. The resulting compactness of the representation of audio content allows index-based retrieval strategies to be exploited for maximizing computational efficiency. A particular application is considered, regarding Cultural Heritage preservation institutions. A methodology is proposed to automatically identify recordings in collections of digitized tapes and vinyl discs. This scenario differs significantly from that of a typical identification task, as a query most often contains more than one relevant result (distinct music work). The audio alignment methodology mentioned above is finally exploited to carry out a precise segmentation of recordings into their individual tracks.
La crescente disponibilità di grandi collezioni multimediali porta all'attenzione problemi di ricerca sempre più complessi in materia di organizzazione e accesso ai dati. Nell'ambito della comunità dell'Information Retrieval è stato raggiunto un consenso generale nel ritenere indispensabili nuovi strumenti di reperimento in grado di superare i limiti delle metodologie basate su meta-dati, sfruttando direttamente l'informazione che risiede nel contenuto multimediale. Lo scopo di questa tesi è lo sviluppo di tecniche per l'allineamento e l'identificazione di contenuti multimediali; la trattazione si focalizza su flussi audio musicali e sequenze numeriche registrate tramite dispositivi di cattura del movimento. Una speciale attenzione è dedicata all'efficienza degli approcci proposti, in particolare per quanto riguarda l'applicabilità in tempo reale degli algoritmi di allineamento e la scalabilità delle metodologie di identificazione. L'allineamento di entità comparabili si riferisce al processo di aggiustamento di caratteristiche strutturali allo scopo di permettere una comparazione diretta tra elementi costitutivi corrispondenti. Questa tesi si concentra sull'allineamento di sequenze rispettivamente ad una sola dimensione, con l'obiettivo di identificare e confrontare eventi significativi in sequenze temporali collegate. L'allineamento di registrazioni musicali alla loro rappresentazione simbolica è il punto di partenza adottato per esplorare differenti metodologie basate su modelli statistici. Si propone un modello unificato per l'allineamento in tempo reale di flussi musicali a partiture simboliche e registrazioni audio. I principali vantaggi sono collegati alla trattazione esplicita del tempo (velocità di esecuzione musicale) nell'architettura del modello statistico; inoltre, ambedue i problemi di allineamento sono formulati sfruttando una rappresentazione continua della dimensione temporale. Un'innovativa applicazione delle tecnologie di allineamento audio è proposta nel contesto della produzione di registrazioni musicali, dove l'intervento umano in attività ripetitive è drasticamente ridotto. L'allineamento di movimenti gestuali è strettamente correlato al contesto dell'allineamento musicale, in quanto gli obiettivi artistici e le soluzioni ingegneristiche delle due aree sono largamente coincidenti. L'espressività di un'esecuzione gestuale è caratterizzata simultaneamente dalla scelta del particolare gesto e dal modo di eseguirlo. Il primo aspetto è collegato ad un problema di riconoscimento, mentre il secondo è affrontato considerando l'evoluzione temporale delle caratteristiche del segnale ed il modo in cui queste differiscono da template pre-registrati. Si propone un modello, strettamente legato alla controparte musicale sopra citata, capace di riconoscere un gesto in tempo reale tra una libreria di templates, simultaneamente allineandolo mentre caratteristiche del segnale come rotazione, dimensionamento e velocità sono congiuntamente stimate. Il drastico incremento delle dimensioni delle collezioni musicali ha portato all'attenzione il problema dell'organizzazione di contenuti multimediali secondo caratteristiche percettive. In particolare, le tecnologie di identificazione basate sul contenuto forniscono strumenti appropriati per reperire e organizzare documenti musicali. Queste tecnologie dovrebbero idealmente essere in grado di identificare una registrazione -- attraverso il confronto con un insieme di registrazioni conosciute -- indipendentemente dalla particolare esecuzione, anche in caso di arrangiamenti o interpretazioni significativamente differenti. Sebbene le tecniche di allineamento assumano un ruolo centrale in letteratura, la metodologia proposta sfrutta strategie solitamente associate al reperimento di informazione testuale. Il calcolo della similarità musicale è basato su tecniche di hashing per creare collisioni fra vettori prossimi nello spazio. La compattezza della risultante rappresentazione del contenuto acustico permette l'utilizzo di tecniche di reperimento basate su indicizzazione, allo scopo di massimizzare l'efficienza computazionale. Un'applicazione in particolare è considerata nell'ambito della preservazione dei Beni Culturali, per l'identificazione automatica di collezioni di nastri e dischi in vinile digitalizzati. In questo contesto un supporto generalmente contiene più di un'opera rilevante. La metodologia di allineamento audio citata sopra è infine utilizzata per segmentare registrazioni in tracce individuali.
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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. First, we design a set of perceptual experiments to investigate the genre-labelling behavior of individuals. The results from these experiments lead us to speculate that multi-label classi cation is more appropriate for classifying music genres. Second, we design a set of computational experiments to evaluate multi-label classi cation algorithms on music. These experiments not only support our speculation but also reveal which algorithms are more suitable for music genre classi cation. Finally, we propose and examine a group of ensemble approaches for combining multi-label classi cation algorithms to further improve classi cation performance. ii
viii, 87 leaves ; 29 cm
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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 machine learning and on empirical testing, the thesis specifies an appropriate evaluation method for feature selection, employs this method to compare existing feature selection algorithms, and evaluates an appropriate feature selection algorithm on the problem of musical genre classification. The outcomes include an increased understanding of the potential for feature selection to benefit MIR and a new technique for optimizing one type of classification-based system.
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Bianchi, Frederick W. "The cognition of atonal pitch structures." Virtual Press, 1985. http://liblink.bsu.edu/uhtbin/catkey/438705.

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The Cognition of Atonal Pitch Structures investigated the ability of a listener to internally organize atonal pitch sequences into hierarchical structures. Based on an information processing model proposed by Deutsch and Feroe (1981), the internal organization of well processed pitch sequences will result in the formation of hierarchical structures. The more efficiently information is processed by the listener, the more organized its internal hierarchical representation in memory. Characteristic of a well organized internal hierarchy As redundancy. Each ensuing level of the hierarchical structure represents a parsimoniuos recoding of the lower levels. In this respect, each higher hierarchical level contains the most salient structural features extracted from lower levels.Because efficient internal organization increases redundancy, more memory space must be allocated to retain a well processed pitch sequence. Based on this assumption, an experiment was conducted to determine the amount of information retained when listening to pre-organized atonal pitch structures and randomly organized pitch structures. Using time duration estimation techniques (Ornstein, 1969; Block, 1974), the relative size of memory allocated for a processing task was determined. Since the subjective experience of time is influenced by the amount of information processed and retained in memory (Ornstein, 1969; Block, 1974), longer time estimations corresponded to larger memory space allocations, and thus, more efficiently organized hierarchical structures.ConclusionThough not significant at the .05 level (p-.21), the results indicate a tendency to suggest that atonal pitch structures were more efficiently organized into internal hierarchical structures than were random pitch structures. The results of the experiment also suggest that a relationship exists between efficient internal hierarchical organization and increased attention and enjoyment. The present study also investigated the influence that other parameters may have on the cognition of pre-organized music. Of interest were the characteristics inherent in music which may facilitate internal organization.
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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 música digital. Según se discute en la tesis,hay toda una serie de tareas a considerar, como la visualización de una colección, la generación de listas de reproducción o la recomendación automática de música. A través de las estimaciones de complejidad musical provistas por los algoritmos descritos, podemos obtener acceso a un nivel de descripción semántica de la música que ofrecerá novedosas e interesantes soluciones para estas tareas.
This thesis proposes a set of algorithms that can be used to compute estimates of music complexity facets from musical audio signals. They focus on aspects of acoustics, rhythm, timbre, and tonality. Music complexity is thereby considered on the coarse level of common agreement among human listeners. The target is to obtain complexity judgments through automatic computation that resemble a naive listener's point of view. The motivation for the presented research lies in the enhancement of human interaction with digital music collections. As we will discuss, there is a variety of tasks to be considered, such as collection visualization, play-list generation, or the automatic recommendation of music. Through the music complexity estimates provided by the described algorithms we can obtain access to a level of semantic music description, which allows for novel and interesting solutions of these tasks.
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SIMONETTA, FEDERICO. "MUSIC INTERPRETATION ANALYSIS. A MULTIMODAL APPROACH TO SCORE-INFORMED RESYNTHESIS OF PIANO RECORDINGS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/918909.

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This Thesis discusses the development of technologies for the automatic resynthesis of music recordings using digital synthesizers. First, the main issue is identified in the understanding of how Music Information Processing (MIP) methods can take into consideration the influence of the acoustic context on the music performance. For this, a novel conceptual and mathematical framework named “Music Interpretation Analysis” (MIA) is presented. In the proposed framework, a distinction is made between the “performance” – the physical action of playing – and the “interpretation” – the action that the performer wishes to achieve. Second, the Thesis describes further works aiming at the democratization of music production tools via automatic resynthesis: 1) it elaborates software and file formats for historical music archiving and multimodal machine-learning datasets; 2) it explores and extends MIP technologies; 3) it presents the mathematical foundations of the MIA framework and shows preliminary evaluations to demonstrate the effectiveness of the approach
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Books on the topic "Music information processing"

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Advances in music information retrieval. Berlin: Springer Verlag, 2010.

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Information retrieval for music and motion. New York: Springer, 2007.

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Müller, Meinard. Information retrieval for music and motion. New York: Springer, 2007.

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Jialie, Shen, ed. Intelligent music information systems: Tools and methodologies. Hershey, PA: Information Science Reference, 2008.

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The strange music of social life: A dialogue on dialogic sociology. Philadelphia: Temple University Press, 2011.

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Music data mining. New York: Taylor & Francis, 2011.

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Taraeva, G. R., and T. F. Shak. Muzyka v informat︠s︡ionnom mire: Nauka, tvorchestvo, pedagogika : sbornik nauchnykh stateĭ = Music in the world of information : science, creative work, pedagogics : collection of articles. Rostov-na-Donu: [Izd-vo Rostovskoĭ gos. konservatorii], 2004.

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H, Chen Homer, ed. Music emotion recognition. Boca Raton, Fla: CRC, 2011.

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Kock, Wiil Uffe, ed. Computer music modeling and retrieval: Second International Symposium, CMMR 2004, Esbjerg, Denmark, May 26-29, 2004 : revised papers. Berlin: Springer, 2005.

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David, Hutchison. Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music: 5th International Symposium, CMMR 2008 Copenhagen, Denmark, May 19-23, 2008 Revised Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Book chapters on the topic "Music information processing"

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Baras, C., N. Moreau, and T. Dutoit. "How could music contain hidden information?" In Applied Signal Processing, 223–63. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-74535-0_7.

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Medhat, Fady, David Chesmore, and John Robinson. "Music Genre Classification Using Masked Conditional Neural Networks." In Neural Information Processing, 470–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_49.

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Zhong, Guoqiang, Haizhen Wang, and Wencong Jiao. "MusicCNNs: A New Benchmark on Content-Based Music Recommendation." In Neural Information Processing, 394–405. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_36.

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Fang, Qianqi, Ling Liu, Junliang Yu, and Junhao Wen. "Meta-path Based Heterogeneous Graph Embedding for Music Recommendation." In Neural Information Processing, 101–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_10.

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Monsignori, M., P. Nesi, and M. B. Spinu. "Watermarking Music Sheets." In Advances in Multimedia Information Processing — PCM 2001, 646–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45453-5_83.

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Sitarek, Tomasz, and Wladyslaw Homenda. "Efficient Processing the Braille Music Notation." In Computer Information Systems and Industrial Management, 338–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33260-9_29.

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Ikeuchi, Ryota, and Kazushi Ikeda. "An Automatic Music Transcription Based on Translation of Spectrum and Sound Path Estimation." In Neural Information Processing, 532–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_64.

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Brewer, Madeline, and Jessica Sharmin Rahman. "Pruning Long Short Term Memory Networks and Convolutional Neural Networks for Music Emotion Recognition." In Neural Information Processing, 343–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_29.

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Duan, Ruo-Nan, Xiao-Wei Wang, and Bao-Liang Lu. "EEG-Based Emotion Recognition in Listening Music by Using Support Vector Machine and Linear Dynamic System." In Neural Information Processing, 468–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34478-7_57.

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Liu, Ning-Han, and Shu-Ju Hsieh. "Intelligent Music Playlist Recommendation Based on User Daily Behavior and Music Content." In Advances in Multimedia Information Processing - PCM 2009, 671–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_59.

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Conference papers on the topic "Music information processing"

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Bozkurt, Baris, Ali Cenk Gedik, and M. Kemal Karaosmanoglu. "Music information retrieval for Turkish music: problems, solutions and tools." In 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136518.

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Simonetta, Federico, Stavros Ntalampiras, and Federico Avanzini. "Multimodal Music Information Processing and Retrieval: Survey and Future Challenges." In 2019 International Workshop on Multilayer Music Representation and Processing (MMRP). IEEE, 2019. http://dx.doi.org/10.1109/mmrp.2019.00012.

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Simonetta, Federico, Stavros Ntalampiras, and Federico Avanzini. "Multimodal Music Information Processing and Retrieval: Survey and Future Challenges." In 2019 International Workshop on Multilayer Music Representation and Processing (MMRP). IEEE, 2019. http://dx.doi.org/10.1109/mmrp.2019.8665366.

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Wang, Tao, Dong-Ju Kim, Kwang-Seok Hong, and Jeh-Seon Youn. "Music Information Retrieval System Using Lyrics and Melody Information." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.283.

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Moh, Yvonne, Peter Orbanz, and Joachim M. Buhmann. "Music preference learning with partial information." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518036.

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Goto, Masataka. "Frontiers of music information research based on signal processing." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7014960.

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Ezzaidi, Hassan, Mohammed Bahoura, and Jean Rouat. "Singer and music discrimination based threshold in polyphonic music." In 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2010. http://dx.doi.org/10.1109/isspit.2010.5711726.

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Abdallah, Samer A., Henrik Ekeus, Peter Foster, Andrew Robertson, and Mark D. Plumbley. "Cognitive music modelling: An information dynamics approach." In 2012 3rd International Workshop on Cognitive Information Processing (CIP). IEEE, 2012. http://dx.doi.org/10.1109/cip.2012.6232940.

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Acici, Koray, Tunc Asuroglu, and Hasan Ogul. "Information retrieval in metal music sub-genres." In 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE, 2017. http://dx.doi.org/10.1109/siu.2017.7960162.

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Huang, Yu-Siang, Szu-Yu Chou, and Yi-Hsuan Yang. "Music thumbnailing via neural attention modeling of music emotion." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282049.

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