一种基于超类别构造的声场景分类惩罚投票算法
A punishment voting algorithm based on super categories construction for acoustic scene classification
摘要：
在声学场景分类研究中，通常将音频片段分割成多个样本。然后利用多数投票来集成样本的结果。本文提出了一种基于超类别构造方法的惩罚投票算法，用于声场景分类。具体来说，我们提出了一个类似于DenseNet的模型作为基本分类器。基础分类器由从原始音频段生成的CQT频谱图训练。利用基分类器的结果，我们提出了一种基于谱聚类的超类别构造方法。进一步训练与所构造的超级类别相对应的超级分类器。最后，利用超级分类器通过惩罚投票来增强基分类器的多数投票。实验表明，惩罚投票在DCASE2017 Development数据集和LITIS Rouen数据集上都明显提高了性能。
英文摘要：
In acoustic scene classification researches, audio segment is usually split into multiple samples. Majority voting is then utilized to ensemble the results of the samples. In this paper, we propose a punishment voting algorithm based on the super categories construction method for acoustic scene classification. Specifically, we propose a DenseNet-like model as the base classifier. The base classifier is trained by the CQT spectrograms generated from the raw audio segments. Taking advantage of the results of the base classifier, we propose a super categories construction method using the spectral clustering. Super classifiers corresponding to the constructed super categories are further trained. Finally, the super classifiers are utilized to enhance the majority voting of the base classifier by punishment voting. Experiments show that the punishment voting obviously improves the performances on both the DCASE2017 Development dataset and the LITIS Rouen dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
