可解释人工智能在音乐中的应用——以Nick Bryan-Kinns教授的“XAI+Music”研究为视角

Meixia Li
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引用次数: 0

摘要

本文主要运用比较分析法和案例分析法对音乐中的可解释性人工智能(XAI)进行分析。在音乐中使用XAI是实时交互的,越具有可解释性和透明度,就越准确。可解释性可以发生在建模过程中或之后,深度学习为XAI提供了理论支持。Nick Bryan-Kinns教授团队通过对“XAI+Music”的实验研究取得突破性进展,将目前人工智能解释的难点,直接应用到创造性的AI模型中,为“XAI+Music”的发展和创新提供参考和思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Use of Explainable Artificial Intelligence in Music—Take Professor Nick Bryan-Kinns’ “XAI+Music” Research as a Perspective
This paper mainly uses the comparative analysis method and the case analysis method to explain the explainable artificial intelligence (XAI) in music. The use of XAI in music is real-time interactive, and the more explainable and transparent, the more accurate it is. Interpretability can occur in or after modeling, and deep learning provides theoretical support for XAI. Professor Nick Bryan-Kinns' team made a breakthrough through experimental research on “XAI+Music” with the difficulties currently being explained by artificial intelligence, XAI technology has been directly applied to the creative AI model, for “XAI+Music” development and innovation provide references and ideas.
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