High-Level Analysis of Audio Features for Identifying Emotional Valence in Human Singing
High-Level Analysis of Audio Features for Identifying Emotional Valence in Human Singing
复制标题
用于识别人类歌唱中情感效价的音频特征的高级分析
DOI：
10.1145/3243274.3243313
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
R. Picking
中科院分区：
文献类型：
--
作者：
Stuart Cunningham;Jonathan Weinel;R. Picking
情感分析仍然是音频和音乐界备受关注的话题。将人类的情感状态与音乐音频的情感内容或意图联系起来的潜力在改善数字音乐库的用户体验和音乐治疗等领域有着广泛的应用领域。对人类无伴奏合唱的情感分析所做的工作较少。最近，Ryerson情感语音和歌曲视听数据库(RAVDESS)发布，其中包括情感验证的人类歌唱样本。在这项工作中，我们应用已建立的音频分析特征来确定这些特征是否可以用于检测人类歌唱中潜在的情感价位。结果表明，短期音频特征：能量；频谱质心(均值)；频谱质心(扩展)；频谱熵；频谱流量；频谱滚降和基频可以作为有用的情绪预测因子，尽管它们在积极和消极情绪中的有效性并不一致。
Emotional analysis continues to be a topic that receives much attention in the audio and music community. The potential to link together human affective state and the emotional content or intention of musical audio has a variety of application areas in fields such as improving user experience of digital music libraries and music therapy. Less work has been directed into the emotional analysis of human acapella singing. Recently, the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) was released, which includes emotionally validated human singing samples. In this work, we apply established audio analysis features to determine if these can be used to detect underlying emotional valence in human singing. Results indicate that the short-term audio features of: energy; spectral centroid (mean); spectral centroid (spread); spectral entropy; spectral flux; spectral rolloff; and fundamental frequency can be useful predictors of emotion, although their efficacy is not consistent across positive and negative emotions.
影响因子：
2.4
作者：
Knox, Don;Beveridge, Scott;MacDonald, Raymond A. R.
通讯作者：
MacDonald, Raymond A. R.
DOI：
--
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Scott Beveridge;Don Knox
通讯作者：
Scott Beveridge;Don Knox