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Multi-Channel Automatic Music Transcription Using Tensor Algebra

arXiv 2107.11250 ↗First posted 23 Jul 2021cs.SD cs.IR cs.LGcited by 0

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Abstract (arXiv)

Music is an art, perceived in unique ways by every listener, coming from acoustic signals. In the meantime, standards as musical scores exist to describe it. Even if humans can make this transcription, it is costly in terms of time and efforts, even more with the explosion of information consecutively to the rise of the Internet. In that sense, researches are driven in the direction of Automatic Music Transcription. While this task is considered solved in the case of single notes, it is still open when notes superpose themselves, forming chords. This report aims at developing some of the existing techniques towards Music Transcription, particularly matrix factorization, and introducing the concept of multi-channel automatic music transcription. This concept will be explored with mathematical objects called tensors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 60 references that are in the map. First posted on arXiv 23 Jul 2021.

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5 of 60 references.

References · 5

  1. 2016TensorLy: Tensor Learning in Pythoncited by 104
  2. 2014The Why and How of Nonnegative Matrix Factorizationcited by 34
  3. 2018Nonnegative PARAFAC2: a flexible coupling approachcited by 11
  4. 2018NIMFA: A Python Library for Nonnegative Matrix Factorizationcited by 10
  5. 2017Dictionary-based Tensor Canonical Polyadic Decompositioncited by 4

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