提出音高强度概念，让音乐生成AI更贴近制作人实际操作。
An introduction to pitch strength in contemporary popular music analysis and production
- 引入音高强度作为低层级感知参数，连接音乐生成与制作实践。
- 音高强度在歌曲内外显著变化，并影响大小结构与和声处理。
- 适合音乐信息检索、生成模型优化及制作人技术研究者参考。
音乐信息检索区分音乐的低层与高层描述。当前生成式AI依赖文本描述，其抽象层级高于录音室音乐人熟悉的控制方式。音高强度是当代流行音乐的一个低层级感知参数，可能使这类AI模型更契合音乐制作需求。信号与感知分析表明，音高强度（1）在歌曲内外差异显著；（2）对小规模与大规模结构均有贡献；（3）有助于处理多声部不协和；（4）可能是上层泛音在感知丰富性视角下可听化的特征。
原文摘要 · Abstract (English)
Music information retrieval distinguishes between low- and high-level descriptions of music. Current generative AI models rely on text descriptions that are higher level than the controls familiar to studio musicians. Pitch strength, a low-level perceptual parameter of contemporary popular music, may be one feature that could make such AI models more suited to music production. Signal and perceptual analyses suggest that pitch strength (1) varies significantly across and inside songs; (2) contributes to both small- and large-scale structure; (3) contributes to the handling of polyphonic dissonance; and (4) may be a feature of upper harmonics made audible in a perspective of perceptual richness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。