A Survey on Automatic Music Transcription

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@article{1704431,
      author = {Pranav Bhagwat, Vishwajit Shelke, Akhilesh Murugkar, Krishiv Dakwala, Shweta C. Dharmadhikari},
      title = {A Survey on Automatic Music Transcription},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {268-274},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704431.pdf},
      abstract = {Automatic Music Transcription (AMT) is a critical but less investigated problem in the field of music information retrieval. In this paper, we study different approaches for achieving Automatic Music Transcription using various methods based on pitch, timbre and note detection. Use of Convolutional Neural Network (CNN) and/or Long Short Term Memory Network (LSTM) is made to transcribe notes from the audio input. We also discuss source separation as a precursor to AMT and different approaches for the same.},
      keywords = {Automatic Music Transcription (AMT), Pitch Detection, Note Detection, Deep Learning, Convolutional Neural Network (CNN), Long Short Term Memory Network (LSTM), Source Separation},
      month = {May},
  }