• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 XLIII Latin American Computer Conference >Music mood classification using visual and acoustic features
【24h】

Music mood classification using visual and acoustic features

机译：使用视觉和听觉特征对音乐心情进行分类

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This work aims to present a system for automatic music mood classification based on acoustic and visual features extracted from the music. The visual features are obtained from spectrograms and the acoustic features are extracted directly from the audio signal. The texture operators used are Local Phase Quantization (LPQ), Local Binary Pattern (LBP) and Robust Local Binary Pattern (RLBP). The acoustic features are described using Rhythm Patterns (RP), Rhythm Histogram (RH) and Statistical Spectrum Descriptor (SSD). The experiments performed were made on a subset of the Latin Music Mood Database, considering three mood classes: positive, negative, and neutral. In the classification step, SVM classifier was used and the final results were taken by using 5-fold cross validation. From several classifiers created performing a zoning strategy along the images, the best individual classifier is that created from the image zone which corresponds to the frequencies from 1,700 Hz to 3,400 Hz, and using the RLBP visual descriptor. In this case, the F-measure obtained is about 59.55%.
机译：这项工作旨在提出一种基于从音乐中提取的声学和视觉特征进行自动音乐情绪分类的系统。视觉特征是从频谱图中获得的，而声学特征是直接从音频信号中提取的。使用的纹理运算符是局部相位量化（LPQ），局部二进制模式（LBP）和鲁棒局部二进制模式（RLBP）。使用节奏模式（RP），节奏直方图（RH）和统计频谱描述符（SSD）描述声学特征。进行的实验是在拉丁音乐心情数据库的一个子集上进行的，其中考虑了三种情绪类别：积极，消极和中性。在分类步骤中，使用SVM分类器，并通过使用5倍交叉验证获得最终结果。从沿图像执行分区策略的几个分类器中，最好的单个分类器是使用RLBP视觉描述符从对应于1,700 Hz至3,400 Hz频率的图像区域创建的分类器。在这种情况下，获得的F值约为59.55％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号