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A robust music genre classification approach for global and regional music datasets evaluation

机译：用于全球和区域音乐数据集评估的可靠音乐流派分类方法

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This paper deals with two problems: (1) the selection of a set of music features in order to achieve high genre classification accuracies; (2) the absence of a representative music dataset of regional Brazilian music. In this paper, we propose a set of features to classify genres of music. The features proposed were obtained by a methodical selection of important features used in the literature of Music Information Retrieval (MIR) and Music Emotion Recognition (MER). Besides, we propose a new music dataset called BMD (Brazilian Music Dataset)1, containing 120 songs labeled in 7 musical genres: FoFFó, Rock, Repente, MPB(Música Popular Brasileira - Brazilian Popular Music), Brega, Sertanejo and Disco. An important characteristic of this new dataset compared with others, is the presence of three popular genres in Brazil Northeast region: Repente, Brega and a characteristic genre similar to MPB, which we also call as MPB. We evaluated our proposed features on both datasets: GTZAN and BMD. The proposed approach achieved average accuracy (after 30 runs of 5-fold-cross-validations) of 79.7% for GTZAN and 86.11% for the BMD. Another important contribution of this work is random repetition of cross-validation executions. Most of the papers performs only a single n-fold cross-validation. We criticize that practice and propose, at least, 30 random executions to compute the average accuracy.
机译：本文涉及两个问题：（1）选择一组音乐特征以实现较高的流派分类精度; （2）缺少巴西地方音乐的代表性音乐数据集。在本文中，我们提出了一组用于对音乐流派进行分类的功能。通过有条不紊地选择在音乐信息检索（MIR）和音乐情感识别（MER）文献中使用的重要特征来获得提出的特征。此外，我们提出了一个新的音乐数据集，称为BMD（巴西音乐数据集）1，其中包含以7种音乐类型标记的120首歌曲：FoFFó，Rock，Repente，MPB（Musica Popular Brasileira-巴西流行音乐），Brega，Sertanejo和Disco。与其他数据集相比，此新数据集的重要特征是在巴西东北部地区存在三种流行类型：Repente，Brega和类似于MPB的特征类型，我们也将其称为MPB。我们在两个数据集（GTZAN和BMD）上评估了我们提出的功能。提出的方法对GTZAN的平均准确度（经过30次5倍交叉验证后）达到了79.7％的平均准确度，对于BMD达到了86.11％的平均准确度。这项工作的另一个重要贡献是交叉验证执行的随机重复。大多数论文仅执行一次n折交叉验证。我们批评这种做法，并建议至少执行30次随机执行以计算平均准确度。

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