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Tool for Piano Practising
Ustinov, Nikita ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
The purpose of this work is to design and implement an application for piano practice. The main disadvantage of existing applications is the limited selection of songs that can be practiced. The application, which is the result of this work, will solve the problem - the user will be able to upload to the application any piano record he wants to practice, and the application will take care of creating a training process. The training process consists of showing the user a certain way of editing notes and simultaneously controlling what the user is playing with a microphone. The biggest challenge of the diploma thesis was to find a way to accurately and quickly classify the audio signal from the microphone. This problem was solved using two independent neural networks with different architectures, which were trained on different data sets. To justify the chosen solution, all the necessary theoretical musical and scientific concepts and methods that are directly related to it will be presented. The resulting application will be tested in three respects: accuracy, speed, the usability of the user.
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Tool for Piano Practising
Ustinov, Nikita ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
The purpose of this work is to design and implement an application for piano practice. The main disadvantage of existing applications is the limited selection of songs that can be practiced. The application, which is the result of this work, will solve the problem - the user will be able to upload to the application any piano record he wants to practice, and the application will take care of creating a training process. The training process consists of showing the user a certain way of editing notes and simultaneously controlling what the user is playing with a microphone. The biggest challenge of the diploma thesis was to find a way to accurately and quickly classify the audio signal from the microphone. This problem was solved using two independent neural networks with different architectures, which were trained on different data sets. To justify the chosen solution, all the necessary theoretical musical and scientific concepts and methods that are directly related to it will be presented. The resulting application will be tested in next respects: accuracy, speed, noise resistance and the usability of the user.
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Tool for Piano Practising
Ustinov, Nikita ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
The purpose of this work is to design and implement an application for piano practice. The main disadvantage of existing applications is the limited selection of songs that can be practiced. The application, which is the result of this work, will solve the problem - the user will be able to upload to the application any piano record he wants to practice, and the application will take care of creating a training process. The training process consists of showing the user a certain way of editing notes and simultaneously controlling what the user is playing with a microphone. The biggest challenge of the diploma thesis was to find a way to accurately and quickly classify the audio signal from the microphone. This problem was solved using two independent neural networks with different architectures, which were trained on different data sets. To justify the chosen solution, all the necessary theoretical musical and scientific concepts and methods that are directly related to it will be presented. The resulting application will be tested in next respects: accuracy, speed, noise resistance and the usability of the user.
|
|
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Tool for Piano Practising
Ustinov, Nikita ; Hrubý, Martin (referee) ; Zbořil, František (advisor)
The purpose of this work is to design and implement an application for piano practice. The main disadvantage of existing applications is the limited selection of songs that can be practiced. The application, which is the result of this work, will solve the problem - the user will be able to upload to the application any piano record he wants to practice, and the application will take care of creating a training process. The training process consists of showing the user a certain way of editing notes and simultaneously controlling what the user is playing with a microphone. The biggest challenge of the diploma thesis was to find a way to accurately and quickly classify the audio signal from the microphone. This problem was solved using two independent neural networks with different architectures, which were trained on different data sets. To justify the chosen solution, all the necessary theoretical musical and scientific concepts and methods that are directly related to it will be presented. The resulting application will be tested in three respects: accuracy, speed, the usability of the user.
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