Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.01 vteřin. 
Web Application for Learning of Guitar Playing
Mikota, Michal ; Černocký, Jan (oponent) ; Szőke, Igor (vedoucí práce)
Tools for teaching musical instruments have been with us for hundreds of years and, like everything else, have gradually adapted to the present day. However, a large number of these applications are overly complex, confusing, and generally unfriendly for their users. At the same time, there are only a handful of apps that have an implemented feature of real-time detection of the played note by the player, which is very desirable and necessary for beginner and moderately advanced guitar players. This bachelor thesis demonstrates and explains the implementation of the various tools needed for teaching using techniques for dominant frequency detection and filtering out unwanted audio artifacts using an algorithm from the field of convolutional neural networks. At the same time, the goal is to focus on the robustness of the algorithm, i.e., it can detect and determine the played tone on the guitar even in noisy environments. The algorithm used by the individual tools developed within this application, combined with a simple user interface, provide the alternative for teaching how to play guitar.
Web Application for Learning of Guitar Playing
Mikota, Michal ; Černocký, Jan (oponent) ; Szőke, Igor (vedoucí práce)
Tools for teaching musical instruments have been with us for hundreds of years and, like everything else, have gradually adapted to the present day. However, a large number of these applications are overly complex, confusing, and generally user-unfriendly for their users. At the same time, there are only a handful of apps that have an implemented feature of real-time detection of the played note by the player, which is very desirable and necessary for beginner and slightly advanced guitar players. This bachelor thesis demonstrates and explains the implementation of the various tools needed for teaching using techniques for dominant frequency detection and filtering out unwanted audio artifacts using an algorithm from the field of convolutional neural networks. At the same time, the goal is to focus on the robustness of the algorithm, i.e., it can detect and determine the played tone on the guitar even in noisy environments. The algorithm used by the individual tools developed within this application, combined with a simple user interface, provide the alternative for teaching how to play guitar

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