基于ACGAN的融合长时尺度图的声场景分类数据增强
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification
摘要：
在声学场景分类中，声学特征在提取场景信息中起着至关重要的作用，场景信息可以存储在不同的时间尺度上。此外，数据集的有限大小可能会导致有偏见的模型，对于来自看不见的城市和令人困惑的场景类的记录性能较差。为了克服这一点，我们提出了一个长期的小波功能，需要一个较低的存储容量，可以更快，更准确地分类与经典的梅尔滤波器组系数（FBank）。该特征可以用类似于FBank的预定义小波尺度来提取。此外，一种新的数据增强方案的基础上生成的对抗性神经网络与辅助分类器（ACGANs）的ASC系统，以提高推广。该方案包含ACGAN和样本过滤器，通过分裂数据集，训练ACGAN并随后过滤样本来迭代地扩展数据库。实验是在来自声学场景和事件的检测和分类（DCASE）挑战的数据集上进行的。DCASE19数据集上的结果表明，与经典的FBank分类器相比，所提出的技术的性能有所改善。此外，所提出的融合系统在DCASE19竞赛中获得了第一名，并超过了DCASE17数据集的最高精度。
英文摘要：
In acoustic scene classification (ASC), acoustic features play a crucial role in the extraction of scene information, which can be stored over different time scales. Moreover, the limited size of the dataset may lead to a biased model with a poor performance for records from unseen cities and confusing scene classes. In order to overcome this, we propose a long-term wavelet feature that requires a lower storage capacity and can be classified faster and more accurately compared with classic Mel filter bank coefficients (FBank). This feature can be extracted with predefined wavelet scales similar to the FBank. Furthermore, a novel data augmentation scheme based on generative adversarial neural networks with auxiliary classifiers (ACGANs) is adopted to improve the generalization of the ASC systems. The scheme, which contains ACGANs and a sample filter, extends the database iteratively by splitting the dataset, training the ACGANs and subsequently filtering samples. Experiments were conducted on datasets from the Detection and Classification of Acoustic Scenes and Events (DCASE) challenges. The results on the DCASE19 dataset demonstrate the improved performance of the proposed techniques compared with the classic FBank classifier. Moreover, the proposed fusion system achieved first place in the DCASE19 competition and surpassed the top accuracies on the DCASE17 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。
