Deep Learning Publication Navigator - subtopic: transcription


Year TitleAuthor
2017   Isolated guitar transcription using a deep belief network  G Burlet, A Hindle
2017   Deep learning models for modeling cellular transcription systems  L Chen
2017   Taking the models back to music practice: evaluating generative transcription models built using deep learning  BL Sturm, O Ben
2017   Context-Independent Polyphonic Piano Onset Transcription with an Infinite Training Dataset  S Li
2017   Rewind: A Transcription Method and Website  C Carthen, V Le, R Kelley, T Kozubowski, FC Harris Jr
2017   Drum Transcription From Polyphonic Music With Recurrent Neural Networks  R Vogl, M Dorfer, P Knees
2017   A deep learning model for predicting transcription factor binding location at single nucleotide resolution  S Salekin, JM Zhang, Y Huang
2017   Automatic Drum Transcription Using The Student-Teacher Learning Paradigm With Unlabeled Music Data  CW Wu, A Lerch
2017   Imputation for transcription factor binding predictions based on deep learning  Q Qin, J Feng
2017   Deep Learning for Jazz Walking Bass Transcription  J Abeßer, S Balke, K Frieler, M Pfleiderer, M Müller
2016   An End-to-End Neural Network for Polyphonic Piano Music Transcription  S Sigtia, E Benetos, S Dixon
2016   Rewind: A Music Transcription Method  CD Carthen
2016   Recurrent Neural Networks For Drum Transcription  R Vogl, M Dorfer, P Knees
2016   On The Potential Of Simple Framewise Approaches To Piano Transcription  R Kelz, M Dorfer, F Korzeniowski, S Böck, A Arzt
2016   Music transcription modelling and composition using deep learning  BL Sturm, JF Santos, O Ben
2015   An End-to-End Neural Network for Polyphonic Music Transcription  S Sigtia, E Benetos, S Dixon
2015   Sequence transcription with deep neural networks  J Ibarz, Y Bulatov, I Goodfellow
2015   Isolated instrument transcription using a deep belief network  G Burlet, A Hindle
2015   Corrigendum: Suppression of vascular permeability and inflammation by targeting of the transcription factor c-Jun  RG Fahmy, A Waldman, G Zhang, A Mitchell, N Tedla