Multi-Channel Automatic Music Transcription Using Tensor Algebra
- URL: http://arxiv.org/abs/2107.11250v1
- Date: Fri, 23 Jul 2021 14:07:40 GMT
- Title: Multi-Channel Automatic Music Transcription Using Tensor Algebra
- Authors: Marmoret Axel, Bertin Nancy, Cohen Jeremy
- Abstract summary: This report aims at developing some of the existing techniques towards Music Transcription.
It will also introduce the concept of multi-channel automatic music transcription.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
Related papers
- MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
We are inspired by how musicians compose music not just from a movie script, but also through visualizations.
We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music.
Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
arXiv Detail & Related papers (2024-06-07T06:38:59Z) - Impact of time and note duration tokenizations on deep learning symbolic
music modeling [0.0]
We analyze the common tokenization methods and experiment with time and note duration representations.
We demonstrate that explicit information leads to better results depending on the task.
arXiv Detail & Related papers (2023-10-12T16:56:37Z) - GETMusic: Generating Any Music Tracks with a Unified Representation and
Diffusion Framework [58.64512825534638]
Symbolic music generation aims to create musical notes, which can help users compose music.
We introduce a framework known as GETMusic, with GET'' standing for GEnerate music Tracks''
GETScore represents musical notes as tokens and organizes tokens in a 2D structure, with tracks stacked vertically and progressing horizontally over time.
Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
arXiv Detail & Related papers (2023-05-18T09:53:23Z) - From Words to Music: A Study of Subword Tokenization Techniques in
Symbolic Music Generation [1.9188864062289432]
Subword tokenization has been widely successful in text-based natural language processing tasks with Transformer-based models.
We apply subword tokenization on post-musical tokenization schemes and find that it enables the generation of longer songs at the same time.
Our study suggests that subword tokenization is a promising technique for symbolic music generation and may have broader implications for music composition.
arXiv Detail & Related papers (2023-04-18T12:46:12Z) - Melody transcription via generative pre-training [86.08508957229348]
Key challenge in melody transcription is building methods which can handle broad audio containing any number of instrument ensembles and musical styles.
To confront this challenge, we leverage representations from Jukebox (Dhariwal et al. 2020), a generative model of broad music audio.
We derive a new dataset containing $50$ hours of melody transcriptions from crowdsourced annotations of broad music.
arXiv Detail & Related papers (2022-12-04T18:09:23Z) - Differential Music: Automated Music Generation Using LSTM Networks with
Representation Based on Melodic and Harmonic Intervals [0.0]
This paper presents a generative AI model for automated music composition with LSTM networks.
It takes a novel approach at encoding musical information which is based on movement in music rather than absolute pitch.
Experimental results show promise as they sound musical and tonal.
arXiv Detail & Related papers (2021-08-23T23:51:08Z) - MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training [97.91071692716406]
Symbolic music understanding refers to the understanding of music from the symbolic data.
MusicBERT is a large-scale pre-trained model for music understanding.
arXiv Detail & Related papers (2021-06-10T10:13:05Z) - Sequence Generation using Deep Recurrent Networks and Embeddings: A
study case in music [69.2737664640826]
This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition.
A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically.
arXiv Detail & Related papers (2020-12-02T14:19:19Z) - Melody-Conditioned Lyrics Generation with SeqGANs [81.2302502902865]
We propose an end-to-end melody-conditioned lyrics generation system based on Sequence Generative Adversarial Networks (SeqGAN)
We show that the input conditions have no negative impact on the evaluation metrics while enabling the network to produce more meaningful results.
arXiv Detail & Related papers (2020-10-28T02:35:40Z) - Optical Music Recognition: State of the Art and Major Challenges [0.0]
Optical Music Recognition (OMR) is concerned with transcribing sheet music into a machine-readable format.
The transcribed copy should allow musicians to compose, play and edit music by taking a picture of a music sheet.
Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach.
arXiv Detail & Related papers (2020-06-14T12:40:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.