Improving Music Genre Classification Using Automatically Induced Harmony Rules
Improving Music Genre Classification Using Automatically Induced Harmony Rules
复制标题
使用自动诱导和声规则改进音乐流派分类
DOI：
10.1080/09298215.2010.525654
复制
发表时间：
2010
影响因子：
1.1
通讯作者：
Anglade A
中科院分区：
文献类型：
--
作者：
Anglade A
我们提出了一个新的体裁分类框架，同时使用低级别的信号为基础的功能和高级别的和谐功能。一个国家的最先进的统计流派分类器的音色特征的基础上扩展使用一阶随机森林包含每个流派的规则来自和声或和弦序列。这个随机森林已经自动诱导，使用一阶逻辑归纳算法TILDE，从数据集，其中每个和弦的程度和和弦类别被识别，并涵盖古典，爵士和流行流派类。基于音频分类器的流派分类器包含206个特征，覆盖音频信号的频谱、时间、能量和音高特性。基于和声的分类器与提取的特征向量的融合在GTZAN和ISMIR04数据集的三个流派子集上进行测试，这些子集分别包含300和448个录音。机器学习分类器使用5 × 5折交叉验证和特征选择进行测试。结果表明，提出的基于和声的规则与基于音色的流派分类系统相结合，提高了流派分类率。
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates.