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Computational Methods for Comparative Music Performance Analysis
Ištvánek, Matěj ; Róka,, Rastislav (referee) ; Jirásek, Ondřej (referee) ; Smékal, Zdeněk (advisor)
Zvyšující se dostupnost digitálního hudebního obsahu a různých interpretací hudebních děl zároveň podněcuje vývoj výpočetních metod oboru získávání hudebních informací (MIR). Tento rozvoj ale nemusí být vždy reflektován v souvisejících oblastech – například v analýze interpretačního výkonu (MPA). Hlavním tématem této disertační práce je využití výpočetních metod a digitálního zpracování hudby pro cíle MPA. Tato práce se zabývá principy MIR pro analýzu a porovnání rozdílů hudebních výkonů a jejich parametrů. Práce analyzuje limitace detektorů nástupů tónů a dob založených na konvenčních přístupech a na strojovém učení, při degradaci vstupního signálu, snížení vzorkovací frekvence, nebo v komplexnějších hudebních strukturách. Dále ukazuje možnosti použití hudební synchronizace, extrakce parametrů a výběru příznaků na originálních datech smyčcových kvartetů pro binární klasifikaci původu interpretů. Na závěr demonstruje vyvíjený software pro komparativní analýzu hudby, který kombinuje přívětivé uživatelské prostředí pro přehrávání, navigaci a vizualizaci dat hudebního výkonu s výpočetními metodami MIR.
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Vamp Plugin for Sonic Visualiser
Pilát, Peter ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
In my Bachelor Thesis, I devote myself to obtaining information from music, the way it can be obtained, the aspect of musical information, and the use of the methods themselves. Then I analyze content-oriented music management methods and also include parameterisation of music recordings and audio signal overall. After familiarizing with the specific parameterization tools to implement the Vamp plugin, which are Sonic Visualiser and Sonic Anotator, I characterize the Vamp Plugin and explain in detail its composition. As explained in the manuals and the calculations in progress, the RMS calculation function of the given signal with the possibility of segmentation functions as well as the function of displaying the sound rate or possible changes in the track temperature. Last but not least, we mention the possible use of these supplements in the future and in different sectors.
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Sound records comparison using timbre features
Miklánek, Štěpán ; Schimmel, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis deals with research of musical features, which are describing music recordings relating to timbre. First chapter deals with historical development and modern approach in a discipline called Music Information Retrieval (MIR), further there is a description of music processing from the perspective of music theory and digital signal processing. Then followed by a description of signal pre-processing. This part is very important when retrieving features from music recordings. In chapter concerned about retrieving features there are summarized all common features used when retrieving information from musical recordings with main concern to timbral features. A database of music recordings and a feature retrieving system is introduced. The last chapter deals with individual analysis of timbral features.
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Music recommendation based on music information retrieval
Semela, René ; Schimmel, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis deals with the design, implementation and testing of the content-based music recommender system based on music information retrieval. In the introduction the attention is paid to issues of music information retrieval and to areas of their utilization, it also focuses on tools of their retrieving. Aferwards the most used types of recommender systems are described, including their typical problems. Options of the hybridization of these systems as well as examples of the popular music recommender systems are mentioned. There also is an outline of their functioning. The following section is focused on the parameterization of musical pieces and is devoted to the description of particular most used parameters. The next section is devoted to the content-based music recommender system design itself, including the defining of particular parameters that are used to differentiate musical recordings using the algorithm mRMR and other procedures. The recommender system design as such is oriented to the classification method k-nearest neighbors. The attention is also paid to the model of user taste recorded by Rocchio algorithm. In the next section the system is implemented according to the design. There is also described its functionality including the background processes. The final part of this work is focused on system testing and evaluation.
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Recognition of music style from orchestral recording using Music Information Retrieval techniques
Jelínková, Jana ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
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 learning classifiers, to be specific: kNN (K-Nearest Neighbor), LDA (Linear Discriminant Analysis), GMM (Gaussian Mixture Models) and SVM (Support Vector Machines). In the final chapter, all the best results are summarized. According to the results, there is significant difference between the Baroque era and the other researched eras. This significant difference led to better identification of the Baroque era recordings. On the contrary, Classical era ended up to be relatively similar to Romantic era and therefore all classifiers had less success in identification of recordings from this era. The results are in line with music theory and characteristics of chosen musical eras.
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A tool for simultaneous playback of multiple composition interpretations
Švejcar, Michael ; Ištvánek, Matěj (referee) ; Miklánek, Štěpán (advisor)
The purpose of this Bachelor’s thesis was to create a piece of software which enables the user to simultaneously play back multiple interpretations of a musical piece and switch between them instantaneously. This was achieved using the App Designer in the MATLAB environment, which is intended for developing applications with graphical user interface. The key to the development of the application was especially the use of available toolboxes and algorithms for computing chromagrams and multiscale dynamic time warping. The final IntSwitcher player enables the user to load two recordings of interpretations of one song. Chromagrams which characterize the individual recordings in terms of tonal development over time are first calculated from the input files. After that, the multiscale dynamic time warping method is applied on the chromagrams, which outputs the warping path. The warping path in this case is a matrix, in which musically corresponding samples of loaded audio files are assigned together with the resolution of 50 ms. From this, the corresponding time position of currently inactive track is computed along with its slider position. If the user switches the currently played recording, the second track starts playing in the same part of composition, even if that part is at a different time in each of the individual recordings. The final software is an appropriate tool for studying differences between various interpretations of the same musical piece.
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Beat tracking systems for music recordings
Staňková, Karolína ; Miklánek, Štěpán (referee) ; Ištvánek, Matěj (advisor)
This master thesis deals with systems for detecting rhythmic structures of music recordings. The field of Music Information Retrieval (MIR) allows us to examine the harmonic and tonal properties of music, rhythm, tempo, etc., and has uses in academic and commercial sphere. Various algorithms are used in the detection of rhythmic structures. However, today, most new methods use neural networks. This work aims to summarize the current research results of systems for detecting beats and tempo, to describe methods of calculating and evaluating the parameters of music recordings, and to implement a program that allows comparison of available detection systems. The result of the work is a script in the Python language, which uses six different systems to detect the rhythmic structure of test recordings. It then checks the outputs of the algorithms according to the given reference and compares the given systems with each other using several evaluation values. It uses two datasets as a reference—one of them is publicly available and the other was created by the author of this thesis (including annotations, i.e., reference beat times, for the sample recordings). The program allows user to see the results in graphs and play any of the sample recordings with detected beat times.
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Music mood and emotion recognition using Music information retrieval techniques
Smělý, Pavel ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
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 subjective emotional annotations. The practical part deals with the design of a static dimensional regression evaluation system for numerical prediction of musical emotions in music recordings, more precisely their position in the AV emotional space. The thesis publishes and comments on the results obtained by individual analysis of the significance of individual parameters and for the overall analysis of the prediction of the proposed model.
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Music genre recognition using Music information retrieval techniques
Zemánková, Šárka ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
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 recognition. The last part of this work describes the results of partial parameter analysis, dependence of genre classification accuracy on the amount of parameters and contains a discussion on the causes of classification accurancy for the individual genres.
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Search in Music Signals
Skála, František ; Szőke, Igor (referee) ; Černocký, Jan (advisor)
This work contains overview of methods used in the area of Music Information Retrieval, mainly for purposes of searching of musical recordings. Several existing services in the areas of music identification and searching are presented and their methods for unique song identification are described. This work also focuses on possible modifications of these algorithms for searching of cover versions of songs and for the possibility of searching based on voice created examples.
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