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Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI 认领 引用 被引量：1
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作者 Nick Bryan-Kinns Bingyuan Zhang +1 位作者 Songyan Zhao Berker Banar 《Machine Intelligence Research》 EI CSCD 2024年第1期29-45,共17页
Generative AI models for music and the arts in general are increasingly complex and hard to understand.The field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understan... Generative AI models for music and the arts in general are increasingly complex and hard to understand.The field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to people.One ap-proach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on gen-erative AI models.This paper contributes a systematic examination of the impact that different combinations of variational auto-en-coder models(measureVAE and adversarialVAE),configurations of latent space in the AI model(from 4 to 256 latent dimensions),and training datasets(Irish folk,Turkish folk,classical,and pop)have on music generation performance when 2 or 4 meaningful musical at-tributes are imposed on the generative model.To date,there have been no systematic comparisons of such models at this level of com-binatorial detail.Our findings show that measureVAE has better reconstruction performance than adversarialVAE which has better musical attribute independence.Results demonstrate that measureVAE was able to generate music across music genres with inter-pretable musical dimensions of control,and performs best with low complexity music such as pop and rock.We recommend that a 32 or 64 latent dimensional space is optimal for 4 regularised dimensions when using measureVAE to generate music across genres.Our res-ults are the first detailed comparisons of configurations of state-of-the-art generative AI models for music and can be used to help select and configure AI models,musical features,and datasets for more understandable generation of music. 展开更多
关键词 Variational auto-encoder explainable AI(XAI) generative music musical features datasets
Methods to Identify Individual Eddy Structures in Turbulent Flow 认领 引用
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作者 Shengwen Wang Jack Goldfeather +1 位作者 Ellen K. Longmire Victoria Interrante 《Tsinghua Science and Technology》 SCIE EI CAS 2013年第2期125-136,共12页
Turbulent flows are intrinsic to many processes in science and engineering, and efforts to elucidate the physics of turbulence are of critical importance to many fields. However, ongoing efforts to achieve a fundament... Turbulent flows are intrinsic to many processes in science and engineering, and efforts to elucidate the physics of turbulence are of critical importance to many fields. However, ongoing efforts to achieve a fundamental understanding of the mechanisms of turbulent flow are hindered by the difficulty of quantifying the complex, nonlinear interactions between individual eddies in these flows. The difficulty of this task is compounded by the lack of robust methods for accurately identifying individual eddy structures and characterizing their dynamic evolution and organization across multiple scales. In this paper we address this problem by proposing several novel approaches for more accurately segmenting individual eddy structures in turbulent flows. 展开更多
关键词 flow visualization segmentation vortices turbulent flow
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