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This paper introduces a guitar tablature recognition system utilizing an artificial neural network. The goal is to provide an easier and quicker visual representation for guitar players. The network is trained using a supervised learning technique called backpropagation, adjusting the weights of each neuron to minimize error. The system extracts guitar tabs from images and converts them into binary matrices. Each matrix is then classified using the trained network. Results show the network's ability to recognize number-patterns not included in the training set, proving its power in pattern recognition. Future work includes incorporating music theory ideas to improve recognition performance.
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Guitar Tablature Recognition Using Artificial Neural Network Introduction To Computational And Biological Vision 2007 Semester A By Ilan Kadar
Tablature(Tab) • Tablature is a form of musical notation. Direct Visual Representation – Easier and Quicker for the player to interpret.
Goal And Motivation Input Output
Training The NetworkSupervised Learning Training Set:={{ , 0}, { , 1}, { , 2 }, { , 4}, { , 8}, { , Blank},…}
Training The NetworkSupervised Learning 4 Learning Technique- Backpropagation Adjust the weights of each neuron to minimize the local error on the entire training set.
Guitar Tab RecognitionMain Stages • Extract the guitar tab from given image
Guitar Tab RecognitionMain Stages • Converting the input image into six vectors Of binary matrices. Vector1( , , , ) Vector2( , , , ) Vector3( , , , ) Vector4( , , , ) Vector5( , , , ) Vector6( , , , )
Guitar Tab RecognitionMain Stages • Classify each of the binary matrices using the trained network. Trained Neural Network 4 Play The Guitar Tabs In 6 Channels
Results,Conclusions And Future Work • The trained network was able to recognize number-patterns which were not part of the training set. • Another proof for the power of Neural Network in Pattern Recognition. Future Work • Use Music Theory ideas to improve the guitar recognition performance.
