摘要
电影背景音乐在强化电影情感、增加情节戏剧性和渲染气氛方面有不可替代的作用。如果能对电影的背景音乐进行面向情感的自动分类,将会提高电影情感内容分析的准确率。针对此问题,构造了电影背景音乐的情感特征向量与电影背景音乐情感分类器,从而改进了背景音乐片断的情感标注。电影背景音乐特征向量由从音乐音频信号中提取的小节长度节奏模式特征、小节长度低音线特征及梅尔频率倒谱系数和音程特征组成。节奏模式和低音线特征与其它特征的不同之处在于其能够反映出电影背景音乐片段的整体节奏结构。引入了概率潜在语义分析(PLSA)方法,构造了电影背景音乐情感分类器,并将电影背景音乐依据情感分为兴奋、紧张、放松和哀伤4类。实验结果表明,构造的电影背景音乐情感特征向量和引入的PLSA分类方法较之现有文献中用到的方法提高了分类的准确率。
Movie background music plays an irreplaceable role in strengthening film affection, heightening the dramatic and rendering atmosphere. If movie background music can be automatically classified by affection, it will be remarkable to improve the accuracy of movie affective content analysis undoubtedly. In view of this, movie background music movie background music affection feature vector and classifier were proposed, so that the annotation of movie background mu- sic was improved. Movie background music affection vector is consisted of bar-long rhythm patterns, bar-long baseline patterns, Mel Frequency Cepstrum Coefficients （MFCC） and interval features extracted from music audio signal. Com- pared with other features, rhythm pattern and baseline pattern features are able to demonstrate rhythm structure over movie background music clip. Probability latent semantic analysis is used to classify the movie background music into excitement, tension, relaxation and sadness. Experimental results show that the movie background music affection fea- ture vector and the PLSA classifier effectively improve the accuracy of classes than the state of the art.
出处
《计算机科学》
CSCD
北大核心
2013年第12期37-40,74,共5页
Computer Science
基金
国家自然科学基金(61173114
60903095
61202300)
湖北省杰出青年基金(2010CDA084)
广东省产学研项目(2011B090400251)资助
关键词
概率潜在语义分析
电影
背景音乐
情感分类
Probability latent semantic analysis, Movie, Background music, Affective-oriented classification
