An End-to-End Neural Network for Polyphonic Piano Music Transcription - Research on AMT that used an acoustic and language model, ~75% accuracy on MAPS.Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription - Using a sequential model to aid in transcription.YIN, a fundamental frequency estimator for speech and music - building off autocorrelation which produces an f0 estimator with even less error.It would be cool to see if/how I could extend this. I wish there were more results shown with metrics like accuracy, but the work seemed clear. Really cool paper for transcribing music using NMF - very simple. Non-negative matrix factorization for polyphonic music transcription.Machine Learning: Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks Related Work on AMT BackgroundÄigital Signal Processing: Fourier Transform, STFT, Constant-Q, Onset/Beat Tracking, Auto-correlation We show that by treating the transcription problem as an image classification problem we can use transformed audio data to predict the group of notes currently being played. In this paper, we plan to build on these works by implementing a novel system to automatically transcribe polyphonic music with an artificial neural network model. There has been a multitude of recent research on using deep learning for music
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