Latent evolutionary signatures: a general framework for analysing music and cultural evolution.
Latent evolutionary signatures: a general framework for analysing music and cultural evolution.
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潜在的进化特征：分析音乐和文化进化的通用框架。
DOI：
10.1098/rsif.2023.0647
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Gerstein,MarkB
中科院分区：
文献类型：
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作者：
Warrell,Jonathan;Salichos,Leonidas;Gancz,Michael;Gerstein,MarkB
文化变革过程与生物进化有许多相似之处。然而，非生物进化的基本单位仍然难以捉摸，尤其是在音乐领域。在这里，我们引入了一个通用框架来共同识别底层单元及其相关的进化过程。我们在和谐和形式等维度上对音乐风格和组织原则进行建模，遵循进化过程。此外，我们建议可以通过从音乐语料库中提取潜在的进化特征来识别此类过程，类似于识别基因组学中的突变特征。这些签名为每首歌曲或音乐作品提供了潜在的嵌入。我们为我们的模型开发了一种深层生成架构，它可以被视为一种变分自动编码器，具有限制潜在空间的进化先验；具体来说，每首歌曲的嵌入通过基于能量的先验联系在一起，这鼓励进化空间中接近的歌曲共享相似的表示。作为说明，我们分析了 McGill Billboard 数据集中的歌曲。我们发现频繁的和弦转换和正式的重复方案，并识别与这些特征相关的潜在进化特征。最后，我们表明，在时期和流派预测等任务中，我们的模型学习到的潜在进化表征优于非进化表征。
Cultural processes of change bear many resemblances to biological evolution. The underlying units of non-biological evolution have, however, remained elusive, especially in the domain of music. Here, we introduce a general framework to jointly identify underlying units and their associated evolutionary processes. We model musical styles and principles of organization in dimensions such as harmony and form as following an evolutionary process. Furthermore, we propose that such processes can be identified by extracting latent evolutionary signatures from musical corpora, analogously to identifying mutational signatures in genomics. These signatures provide a latent embedding for each song or musical piece. We develop a deep generative architecture for our model, which can be viewed as a type of variational autoencoder with an evolutionary prior constraining the latent space; specifically, the embeddings for each song are tied together via an energy-based prior, which encourages songs close in evolutionary space to share similar representations. As illustration, we analyse songs from the McGill Billboard dataset. We find frequent chord transitions and formal repetition schemes and identify latent evolutionary signatures related to these features. Finally, we show that the latent evolutionary representations learned by our model outperform non-evolutionary representations in such tasks as period and genre prediction.