论文标题
音乐流派分类的开放式识别
Open Set Recognition For Music Genre Classification
论文作者
论文摘要
我们使用开源GTZAN和FMA数据集探索已知和未知类型类别的分割。对于每个人，我们从最佳封闭式集合体裁分类开始，然后应用开放式识别方法。我们为使用OSR提供了一种用于音乐流派分类任务的算法。我们证明了检索已知流派的能力，并鉴定了新型流派的听觉模式（不出现在训练集中）。我们使用GTZAN和FMA数据集进行了四个实验，每个实验包含不同的已知和未知类别集，以建立新型流派检测的基线能力。我们在OpenMax和SoftMax上都采用网格搜索来确定每个实验设置的最佳总分类精度，并说明类型标签和开放集识别精度之间的相互作用。
We explore segmentation of known and unknown genre classes using the open source GTZAN and FMA datasets. For each, we begin with best-case closed set genre classification, then we apply open set recognition methods. We offer an algorithm for the music genre classification task using OSR. We demonstrate the ability to retrieve known genres and as well identification of aural patterns for novel genres (not appearing in a training set). We conduct four experiments, each containing a different set of known and unknown classes, using the GTZAN and the FMA datasets to establish a baseline capacity for novel genre detection. We employ grid search on both OpenMax and softmax to determine the optimal total classification accuracy for each experimental setup, and illustrate interaction between genre labelling and open set recognition accuracy.
