This study aims to recognise emotions in music through the Adaptive-Network-Based Fuzzy (ANFIS). For this, we applied such structure in 877 MP3 files with thirty seconds duration each, collected directly on the YouTube platform, which represent the emotions anger, fear, happiness, sadness, and surprise. We developed four classification strategies, consisting of sets of five, four, three, and two emotions. The results were considered promising, especially for three and two emotions, whose highest hit rates were 65.83% for anger, happiness and sadness, and 88.75% for anger and sadness. A reduction in the hit rate was observed when the emotions fear and happiness were in the same set, raising the hypothesis that only the audio content is not enough to distinguish between these emotions. Based on the results, we identified potential in the application of the ANFIS framework for problems with uncertainty and subjectivity.

本研究旨在通过基于自适应网络的模糊 (ANFIS) 来识别音乐中的情绪。为此，我们在 YouTube 平台上直接收集的 877 个 MP3 文件中应用了这种结构，每个文件时长为 30 秒，分别代表愤怒、恐惧、快乐、悲伤和惊讶的情绪。我们开发了四种分类策略，包括五种、四种、三种和两种情绪的集合。结果被认为是有希望的，尤其是对于三种和两种情绪，愤怒、快乐和悲伤的命中率最高为65.83%，愤怒和悲伤的命中率最高为88.75%。当恐惧和快乐情绪处于同一组时，命中率会降低，这提出了仅音频内容不足以区分这些情绪的假设。根据结果​​，