Scholarlink

<p>Music in the current digital era is consumed primarily on-demand, for example by streaming from Spotify or YouTube, rather than by broadcasting, for example, by listening to a radio station. A strong correlation has been found between music and emotions. Hence, with our ability to select our own music and the availability of artificial intelligence (AI) tools, the importance of music emotion recognition (MER) is significant. This research proposes a novel methodology for predicting emotions from a musical piece that is selected by the listener based on a two-layer AI. The dataset and the process are exceptional in two ways: first, by their qualitative character, which minimized the inherent subjectivity of self-reported emotions, and also the high reliability of the collected data in comparison to other methods such as a questionnaire; and second, by their quantitative character, given <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:n=\text{9,090}\)</EquationSource> </InlineEquation> songs from about 4,447 people. The model succeeded in predicting the primary emotion with <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:accuracy=57\%\)</EquationSource> </InlineEquation> and one of the two leading emotions (the primary and the secondary) with <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:accuracy=75.1\%\)</EquationSource> </InlineEquation>, and without accommodating demographic factors in the model. We believe this knowledge may be a useful tool for therapists, coaches, teachers, etc. and may also raise public awareness of a potential privacy violation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The sound of emotions: an artificial intelligence approach to predicting emotions from musical selections

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

查看全文
  • Ron S. Hirschprung,
  • Ori Leshman,
  • Chen Hajaj

摘要

Music in the current digital era is consumed primarily on-demand, for example by streaming from Spotify or YouTube, rather than by broadcasting, for example, by listening to a radio station. A strong correlation has been found between music and emotions. Hence, with our ability to select our own music and the availability of artificial intelligence (AI) tools, the importance of music emotion recognition (MER) is significant. This research proposes a novel methodology for predicting emotions from a musical piece that is selected by the listener based on a two-layer AI. The dataset and the process are exceptional in two ways: first, by their qualitative character, which minimized the inherent subjectivity of self-reported emotions, and also the high reliability of the collected data in comparison to other methods such as a questionnaire; and second, by their quantitative character, given \(\:n=\text{9,090}\) songs from about 4,447 people. The model succeeded in predicting the primary emotion with \(\:accuracy=57\%\) and one of the two leading emotions (the primary and the secondary) with \(\:accuracy=75.1\%\) , and without accommodating demographic factors in the model. We believe this knowledge may be a useful tool for therapists, coaches, teachers, etc. and may also raise public awareness of a potential privacy violation.