论文标题
基于声学段的基于部分段的段单元选择方法，用于与部分话语进行声音分类
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances
论文作者
论文摘要
在本文中，我们提出了一个亚物质单元选择框架，以删除音频记录中的原声段，这些音段几乎没有信息进行声学场景分类（ASC）。我们的方法建立在涵盖整个声学场景空间的一组通用的声学片段单元上。首先，这些单元是用用于将声学场景的声音将其化为声段单元序列的声学段模型（ASM）建模的。接下来，与信息检索中的停止单词的想法并行，自动检测到停止ASM。最后，与停止ASM相关的声段被阻塞，因为它们的索引功率低，可以检索大多数声学场景。与具有整个话语的构建场景模型相反，ASM检查的子量，即没有停止声学片段的声音话语，然后用作Alexnet-L后端的输入，以进行最终分类。在DCASE 2018数据集中，场景分类精度从68％，整个话语增加到72.1％，并选择了细分市场。这代表了没有任何数据增强和/或集成策略的竞争精度。此外，我们的方法与Alexnet-l相比，引起了人们的注意。
In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (ASC). Our approach is built upon a universal set of acoustic segment units covering the overall acoustic scene space. First, those units are modeled with acoustic segment models (ASMs) used to tokenize acoustic scene utterances into sequences of acoustic segment units. Next, paralleling the idea of stop words in information retrieval, stop ASMs are automatically detected. Finally, acoustic segments associated with the stop ASMs are blocked, because of their low indexing power in retrieval of most acoustic scenes. In contrast to building scene models with whole utterances, the ASM-removed sub-utterances, i.e., acoustic utterances without stop acoustic segments, are then used as inputs to the AlexNet-L back-end for final classification. On the DCASE 2018 dataset, scene classification accuracy increases from 68%, with whole utterances, to 72.1%, with segment selection. This represents a competitive accuracy without any data augmentation, and/or ensemble strategy. Moreover, our approach compares favourably to AlexNet-L with attention.
