用新符号音乐表示法生成多轨乐曲，让音乐更和谐自然。
MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT
- 设计新符号音乐表示法，融入和弦分析，提升信息完整性。
- 基于MusicBERT微调的判别器结合相对标准损失，生成音乐更连贯。
- 适合音乐生成、人工智能作曲研究者，尤其关注多轨协同创作。
我们提出一种专为符号化多轨音乐生成设计的新颖符号音乐表示方法与生成对抗网络（GAN）框架。当前符号音乐生成技术主要面临两大挑战：训练数据缺乏和弦与调式信息，以及需针对符号音乐格式定制特殊模型架构。本文通过引入MusicLang和弦分析模型构建新表示法，并提出适配该表示的MMT-BERT架构。为构建鲁棒的多轨音乐生成器，我们对预训练的MusicBERT模型进行微调作为判别器，并引入相对标准损失。该方法借助MusicBERT中编码的符号音乐深层语义，增强了生成音乐的协和性与人文感。实验结果表明，本方法在遵循当前最先进范式的基础上，显著提升了生成质量。
原文摘要 · Abstract (English)
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.
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