Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text as deeply as they do in music. The ability of a computational model to interpret musical emotions is largely unexplored and could have implications and uses in therapy and musical queuing. In this paper, two individual tasks are addressed. This study seeks to (1) predict the emotion of a musical clip over time and (2) determine the next emotion value after the music in a time series to ensure seamless transitions. Utilizing data from the Emotions in Music Database, which contains clips of songs selected from the Free Music Archive annotated with levels of valence and arousal as reported on Russel's circumplex model of affect by multiple volunteers, models are trained for both tasks. Overall, the performance of these models reflected that they were able to perform the tasks they were designed for effectively and accurately.

中文翻译：

---

音乐中基于音频的情感分析的探索和应用

情感分析是文本处理的一个不断探索的领域，涉及文本的观点、情感和主观性的计算分析。然而，这个想法不仅限于文本和语音，事实上，它可以应用于其他模式。事实上，人类在文本中表达自己的方式并不像在音乐中那样深刻。计算模型解释音乐情感的能力在很大程度上尚未被探索，并且可能在治疗和音乐排队中产生影响和用途。在本文中，解决了两个单独的任务。本研究旨在 (1) 预测音乐剪辑随时间的情感变化，以及 (2) 确定时间序列中音乐之后的下一个情感值，以确保无缝过渡。利用音乐数据库中的情感数据，该数据库包含从免费音乐档案中选择的歌曲片段，并根据多个志愿者的罗素情感循环模型报告的效价和唤醒水平进行了注释，模型针对这两项任务进行了训练。总体而言，这些模型的性能反映出它们能够有效且准确地执行设计任务。