MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT

• URL: http://arxiv.org/abs/2409.00919v1
• Date: Mon, 2 Sep 2024 03:18:56 GMT
• Title: MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT
• Authors: Jinlong Zhu, Keigo Sakurai, Ren Togo, Takahiro Ogawa, Miki Haseyama,
• Abstract summary: We propose a novel symbolic music representation and Generative Adversarial Network (GAN) framework specially designed for symbolic multitrack music generation. To build a robust multitrack music generator, we fine-tune a pre-trained MusicBERT model to serve as the discriminator, and incorporate relativistic standard loss.
• Score: 44.204383306879095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel symbolic music representation and Generative Adversarial Network (GAN) framework specially designed for symbolic multitrack music generation. The main theme of symbolic music generation primarily encompasses the preprocessing of music data and the implementation of a deep learning framework. Current techniques dedicated to symbolic music generation generally encounter two significant challenges: training data's lack of information about chords and scales and the requirement of specially designed model architecture adapted to the unique format of symbolic music representation. In this paper, we solve the above problems by introducing new symbolic music representation with MusicLang chord analysis model. We propose our MMT-BERT architecture adapting to the representation. To build a robust multitrack music generator, we fine-tune a pre-trained MusicBERT model to serve as the discriminator, and incorporate relativistic standard loss. This approach, supported by the in-depth understanding of symbolic music encoded within MusicBERT, fortifies the consonance and humanity of music generated by our method. Experimental results demonstrate the effectiveness of our approach which strictly follows the state-of-the-art methods.

Related papers

• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey [6.416887247454113]
  This survey reviews NLP methods applied to symbolic music generation and information retrieval studies. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms.
  arXiv  Detail & Related papers  (2024-02-27T12:48:01Z)
• The Music Meta Ontology: a flexible semantic model for the interoperability of music metadata [0.39373541926236766]
  We introduce the Music Meta ontology to describe music metadata related to artists, compositions, performances, recordings, and links. We provide a first evaluation of the model, alignments to other schemas, and support for data transformation.
  arXiv  Detail & Related papers  (2023-11-07T12:35:15Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• 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)
• Symphony Generation with Permutation Invariant Language Model [57.75739773758614]
  We present a symbolic symphony music generation solution, SymphonyNet, based on a permutation invariant language model. A novel transformer decoder architecture is introduced as backbone for modeling extra-long sequences of symphony tokens. Our empirical results show that our proposed approach can generate coherent, novel, complex and harmonious symphony compared to human composition.
  arXiv  Detail & Related papers  (2022-05-10T13:08:49Z)
• 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)

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.