learning (artificial intelligence); music; pattern classification; 10-dimensional feature vector; audio features; dimension reduction; distance metric learning algorithm; feature concatenation; feature vector dimensionality; long feature vector; low dimensional projection; music genre classification problem; very short feature vector; widely-used dataset; Accuracy; Classification algorithms; Feature extraction; Measurement; Mel frequency cepstral coefficient; Principal component analysis; Support vector machine classification; dimension reduction; distance metric learning; music genre classification;
机译:短期特征空间和音乐流派分类
机译:基于谱时特征和特征选择的音乐流派分类系统
机译:Δ距离:由多级特征向量表示的图像之间的一组相异度度量
机译:基于距离度量学习的音乐类型分类非常短的特征向量
机译:高维生物信息学数据中最近邻距离的特征选择的新特性和理论特性
机译:古典爵士和摇滚音乐家的听觉特征:对音乐声音特征的体裁敏感性
机译:使用基于内容的混合特征向量改进音乐体裁的自动分类