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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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Research of tempo features comparing audio records
Ištvánek, Matěj ; Galáž, Zoltán (referee) ; Kiska, Tomáš (advisor)
This thesis deals with technical properties of audio signals or more precisely of recordings from the prepared database and describes parameters which are used for music transcription and analysis of audio signals. It summarises information about music theory and automatic transcription of audio recordings, introduces specialist studies that deal with problems of signal analysis and their results. Furthermore it mentions attributes with the best ability to generally differentiate included songs from rhythmical and metrical aspects. Thesis analyses in the MATLAB language, from the prepared database, all interpretations of the piece "String Quartet No. 1 – IV. Con moto" from Leoš Janáček with two selected methods and shows results of the analysis and comparing of the methods. Finally the work summarizes all information and problems of the thesis.
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Image codecs based on the wavelet transform
Kiska, Tomáš ; Říha, Kamil (referee) ; Rajmic, Pavel (advisor)
This bachelor´s thesis describes issues of wavelet compression, which is today already in common use mainly at work with digital photographs or other pictures. Using these algorithm you can copy informations and save efficiently. There are main image formats and types of compression suitable for editing pictures desribed in this thesis. So the focus is on discrete wavelet transform, which uses so-called wavelets, the Haar´s and Daubechies wavelet are the best known of all. Next chapter of this work explores colour spaces, in which can be a picture displayed. In case of coding and encoding it is model RGB and YCbCr. Not least there is mentioned term of picture decomposition. At the end, there are image codecs EZW, SPITH and EBCOT which are describe in detail.
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Acoustic analysis of sentences complicated for articulation in patients with Parkinson's disease
Kiska, Tomáš ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VAI, etc.). The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. A protocol of dysarthric speech acquisition is described in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, new features based on method RASTA. The analysis is based on parametrization sentences complicated for articulation. Experimental dataset consists of 101 PD patients with different disease progress and 53 healthy controls. For classification with feature selection have selected method mRMR.
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Application for the calculation of speech features describing hypokinetic dysarthria
Hynšt, Miroslav ; Mekyska, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis is about design and implementation of application for computing speech parameters on people with Parkinson disease. At the beginning is generaly described Parkinson disease and Hypokinetic dysarthria and how it affects the speech and speech parameters when it occurs. Mainly there are described areas of speech like phonation, prosody, articulation and fluent speech. As a part of next topic this thesis describes specific speech parameters with bigger meaning during diagnosis Parkinson disease and it's progress over the time. There are also mentioned few significant studies dealing with examination of speech of the subjects with diagnoses of Parkinson disease and computing some speech parameters in order to analyze their speech impairments. Part of the thesis is description of implemented standalone application for calculating, exporting and visualizing of speech parameters from selected sound records.
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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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