【摘要】：為提升音樂情感識別的準確率,提出基于中高層特征的音樂情感識別模型,摒棄頻譜特性、色度、諧波系數(shù)等低層特征,以更接近于人認知的中高層特征包括和弦、節(jié)拍、速度、調(diào)式、樂器種類、織體、旋律走勢等作為情感識別模型的輸入。建立一個包含385個音樂片斷的數(shù)據(jù)集,將音樂情感識別抽象為一個回歸問題,采用機器學習算法進行學習,預(yù)測音樂片段的8維情感向量。實驗結(jié)果表明,相比低層特征,采用中高層特征作為輸入時的準確率R2能夠從59.6%提高至69.8%。
[Abstract]:In order to improve the accuracy of music emotion recognition, a music emotion recognition model based on middle and high level features is proposed. The lower features, such as spectrum characteristics, chroma, harmonic coefficients and so on, are abandoned, so that the middle and high level features, which are closer to human cognition, include chords and rhythms. Speed, mode, musical instrument type, texture, melody trend, etc., as the input of emotion recognition model. A data set consisting of 385 pieces of music was established. The recognition of music emotion was abstracted into a regression problem. Machine learning algorithm was used to study and predict the 8-dimensional emotion vector of music segment. The experimental results show that, compared with the lower level features, R2 can improve the accuracy from 59.6% to 69.8% when using middle and high level features as input.
【作者單位】： 復(fù)旦大學電子工程系;復(fù)旦大學信息學院智慧網(wǎng)絡(luò)與系統(tǒng)研究中心;
【分類號】：TP181;TN912.34
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本文編號：2378372