论文标题
使用听力数据的情绪分类
Mood Classification Using Listening Data
论文作者
论文摘要
歌曲的心情是大量音乐集中探索和推荐的高度相关功能。这些收集倾向于需要自动方法来预测这种情绪。在这项工作中，我们表明，在对情绪进行分类时，基于聆听的功能优于基于内容的功能：通过听力数据的矩阵分解获得的嵌入似乎比基于其音频内容的嵌入更多的曲目情绪更具信息。为了证明这一点，我们编制了一部分，其中一部分为67K曲目，并收集了从Allmusic收集的188种不同情绪的专家注释。我们在这个新颖的数据集上的结果不仅暴露了当前基于音频的模型的局限性，而且还旨在促进对这个及时主题的进一步可重现研究。
The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music. These collections tend to require automatic methods for predicting such moods. In this work, we show that listening-based features outperform content-based ones when classifying moods: embeddings obtained through matrix factorization of listening data appear to be more informative of a track mood than embeddings based on its audio content. To demonstrate this, we compile a subset of the Million Song Dataset, totalling 67k tracks, with expert annotations of 188 different moods collected from AllMusic. Our results on this novel dataset not only expose the limitations of current audio-based models, but also aim to foster further reproducible research on this timely topic.
