MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT
MMT-BERT combines Multitrack Music Transformer and MusicBERT for chord-aware symbolic music generation, achieving 99.73% PCES.
Key Findings
Methodology
This study introduces a novel symbolic music representation using the MusicLang chord analysis model and employs the MMT-BERT architecture. The generator uses a Multitrack Music Transformer, while the discriminator is a fine-tuned MusicBERT model, incorporating relativistic standard loss to optimize the training process.
Key Results
- Result 1: MMT-BERT achieved 99.73% in PCES, significantly outperforming other methods.
- Result 2: It also excelled in SCS and GCS, reaching 99.64% and 99.66%, respectively.
- Result 3: Removing chord events or MusicBERT reduces performance, confirming their importance.
Significance
By introducing chord information and using MusicBERT as the discriminator, this study significantly enhances the harmony and human-like expression of generated music, addressing the lack of chord information in previous methods.
Technical Contribution
Technical contributions include proposing a symbolic music representation with chord information and using MusicBERT as a discriminator in GANs for the first time, improving model stability and music quality.
Novelty
This study is the first to combine MusicLang chord analysis with MusicBERT for symbolic music generation, significantly enhancing music harmony and structure.
Limitations
- Limitation 1: The model's handling of chord changes still needs improvement, especially in complex chord progressions.
- Limitation 2: The length of generated music is limited, not fully utilizing long-sequence information.
Future Work
Future work can explore more complex chord progression handling and increase the length and diversity of generated music to further enhance model performance.
AI Executive Summary
Symbolic music generation is a crucial research area in music creation, yet existing methods fall short in handling chord information, resulting in less human-like music. The proposed MMT-BERT combines Multitrack Music Transformer and MusicBERT, significantly enhancing the harmony and human-like expression of generated music by introducing chord information and using MusicBERT as a discriminator.
The core technology of MMT-BERT lies in its symbolic music representation, which integrates the MusicLang chord analysis model to automatically extract and encode chord information. The generator uses a Multitrack Music Transformer capable of handling multi-dimensional inputs and outputs, while the discriminator is a fine-tuned MusicBERT model leveraging its deep understanding of symbolic music to improve GAN performance.
Experimental results show that MMT-BERT excels in PCES, SCS, and GCS metrics, with music quality and length surpassing existing methods. However, the model still has room for improvement in handling complex chord progressions and generating long-sequence music, which will be explored in future work.
Deep Analysis
Background
Symbolic music generation is a vital area of music creation, with early studies based on music theory and Markov chains. Recently, deep learning methods like GANs and Transformers have significantly improved music generation quality and speed. However, existing methods lack chord information processing, resulting in less human-like music.
Core Problem
Existing symbolic music generation methods lack chord information processing, resulting in less human-like music. Additionally, the ever-changing format of symbolic music poses challenges for model architecture design.
Innovation
This study introduces a novel symbolic music representation using the MusicLang chord analysis model and employs the MMT-BERT architecture. The generator uses a Multitrack Music Transformer, while the discriminator is a fine-tuned MusicBERT model, incorporating relativistic standard loss to optimize the training process.
Methodology
- �� Symbolic Music Representation: Integrates MusicLang chord analysis to automatically extract and encode chord information.
- �� Generator: Uses Multitrack Music Transformer to handle multi-dimensional inputs and outputs.
- �� Discriminator: Fine-tuned MusicBERT model leveraging its deep understanding of symbolic music.
- �� Loss Function: Incorporates relativistic standard loss to improve training stability.
Experiments
The experiment uses the Symbolic Orchestral Database (SOD), containing 5,864 MIDI-encoded music pieces. The dataset is divided into training, testing, and validation sets, with 80%, 10%, and 10% respectively. The Adagrad optimizer is used, and data augmentation is applied.
Results
Experimental results show that MMT-BERT excels in PCES, SCS, and GCS metrics, with music quality and length surpassing existing methods. Removing chord events or MusicBERT reduces performance, confirming their importance.
Applications
This method can be used in automatic music creation, music education, and music therapy, helping musicians and educators generate high-quality music pieces.
Limitations & Outlook
The model still has room for improvement in handling complex chord progressions and generating long-sequence music, which will be explored in future work.
Plain Language Accessible to non-experts
Imagine a music factory where different machines handle different tasks. MMT-BERT is like a smart robot in the factory that can automatically generate new music pieces based on input music information. This robot is particularly clever because it understands chord information in music, like a music master who knows exactly which chord to use when. In this way, the music generated by MMT-BERT sounds more harmonious and human-like.
ELI14 Explained like you're 14
Imagine you're playing a music game where you can choose different instruments and chords to create music. MMT-BERT is like a super helper in the game that can automatically generate music for you, and these pieces sound like they were made by professional musicians. This helper is amazing because it understands chord information in music, like a music master who knows exactly which chord to use when. So, you can easily create great-sounding music!
Glossary
Symbolic Music
Music represented using symbols or numbers, like MIDI format.
In this paper, symbolic music refers to encoded MIDI music data.
Generative Adversarial Network (GAN)
A neural network composed of a generator and a discriminator used for generating data.
GAN framework is used in this paper to generate symbolic music.
Transformer
A neural network architecture based on self-attention mechanism, commonly used in NLP.
The paper uses Multitrack Music Transformer as the generator.
MusicBERT
A pre-trained model for symbolic music understanding.
MusicBERT is used as the discriminator in this paper.
Chord Analysis
The process of extracting chord information from music.
MusicLang is used for chord analysis in this paper.
Open Questions Unanswered questions from this research
- 1 Handling complex chord progressions remains an unsolved issue, with current methods performing limitedly.
- 2 The ability to generate long-sequence music needs improvement to fully utilize long-sequence information.
Applications
Immediate Applications
Automatic Music Creation
Musicians can use this method to quickly generate high-quality music pieces, saving creation time.
Music Education
Educators can use this method to provide students with rich music materials, helping them understand chord structures.
Long-term Vision
Music Therapy
By generating personalized music pieces, it helps patients with emotional regulation and psychological therapy.
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.