English

Multi-Channel Automatic Music Transcription Using Tensor Algebra

Sound 2021-07-26 v1 Information Retrieval Machine Learning Audio and Speech Processing

Abstract

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.

Keywords

Cite

@article{arxiv.2107.11250,
  title  = {Multi-Channel Automatic Music Transcription Using Tensor Algebra},
  author = {Axel Marmoret and Nancy Bertin and Jeremy Cohen},
  journal= {arXiv preprint arXiv:2107.11250},
  year   = {2021}
}

Comments

40 pages, 14 figues, 5 tables, code can be found at: https://gitlab.inria.fr/amarmore/nonnegative-factorization

R2 v1 2026-06-24T04:27:53.367Z