This work advances the music emotion recognition state-of-the-art by proposing novel emotionally-relevant audio features. We reviewed the existing audio features implemented in well-known frameworks and their relationships with the eight commonly defined musical concepts. This knowledge helped uncover musical concepts lacking computational extractors, to which we propose algorithms - namely related with musical texture and expressive techniques. To evaluate our work, we created a public dataset of 900 audio clips, with subjective annotations following Russell's emotion quadrants. The existent audio features (baseline) and the proposed features (novel) were tested using 20 repetitions of 10-fold cross-validation. Adding the proposed features improved the F1-score to 76.4 percent (by 9 percent), when compared to a similar number of baseline-only features. Moreover, analysing the features relevance and results uncovered interesting relations, namely the weight of specific features and musical concepts to each emotion quadrant, and warrant promising new directions for future research in the field of music emotion recognition, interactive media, and novel music interfaces.

中文翻译：

---

用于音乐情感识别的新音频特征

这项工作通过提出新的与情感相关的音频特征来推进音乐情感识别的最新技术。我们回顾了在众所周知的框架中实现的现有音频功能及其与八个常见音乐概念的关系。这些知识有助于发现缺乏计算提取器的音乐概念，我们为此提出了算法——即与音乐纹理和表现技巧相关的算法。为了评估我们的工作，我们创建了一个包含 900 个音频剪辑的公共数据集，并在 Russell 的情绪象限之后进行了主观注释。使用 10 折交叉验证的 20 次重复测试现有的音频特征（基线）和提议的特征（新颖）。添加建议的功能将 F1 分数提高到 76.4%（提高 9%），与类似数量的仅基线特征相比。此外，分析特征相关性和结果发现了有趣的关系，即特定特征和音乐概念对每个情感象限的权重，为音乐情感识别、交互媒体和新音乐界面领域的未来研究提供了有希望的新方向。