AIGC音乐的基本原理

AIGC音乐生成依托人工智能模型，通常采用深度学习（Deep Learning）、生成对抗网络（GAN）、变分自编码器（VAE）以及近年流行的Transformer架构。模型通过对大量乐谱、音频和MIDI数据的学习，建立旋律走向、和声结构、节奏模式的统计规律，从而在接收用户的文本提示（Prompt）后生成符合要求的音乐。
AIGC music generation relies on AI models, often using Deep Learning, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer architectures. By training on large datasets of sheet music, audio, and MIDI files, these models learn statistical rules of melody, harmony, and rhythm. When provided with a user prompt, they generate music accordingly.

AIGC音乐的应用场景

AIGC音乐在传媒与艺术设计中有广泛应用，例如：

AIGC music has wide applications in media and art design, such as:

短视频与广告的背景音乐

游戏与动画的配乐

虚拟IP与数字品牌的主题曲

个人创作与实验性音乐探索

Background music for short videos and advertisements

Soundtracks for games and animations

Theme songs for virtual IPs and digital brands

Personal creative and experimental music projects

AIGC音乐的优势与局限 / Advantages and Limitations

优势：

• 降低创作门槛，非专业用户也可快速生成音乐。

• 大幅缩短音乐制作周期。

• 能够覆盖不同风格，生成多样化作品。

  
  

• Advantages:

Lowers the entry barrier, enabling non-professionals to compose.

Shortens production cycles significantly.

Supports multi-style, diverse outputs

局限：

• 生成结果缺乏原创性，容易同质化。

• 对复杂交响结构、长篇音乐的把控有限。

• 版权与归属尚不明确，存在争议。

  
  

Limitations:

Lack of originality, risk of homogeneity.

Weak at handling long/complex compositions.

Copyright and ownership issues remain unresolved.