Abstract
In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.
Abstract (translated)
本文提出了一种新的声场景分类策略,即通过识别不同的声音事件来识别声场景。这与现有的策略不同,这些策略专注于表征全球音频分布或短期音频特征的时间演变,而不分析声音事件的级别。为了识别每个场景的不同声音事件,我们在多实例学习(mil)框架中构建了ASC,其中每个音频记录都映射到一个实例包表示中。在这里,实例可以看作是场景中声音事件的高级表示。我们还提出了一个mil神经网络模型,它隐式地识别不同的实例(即声音事件)。此外,我们还提出了两个专门设计的模块,分别模拟声音事件的多时间尺度和多模态性质。实验是在dcase2018任务1子任务B的官方开发集上进行的,我们的最佳性能模型在分类精度方面比官方基线提高了9.4%(68.3%对58.9%)。研究表明,通过识别不同的声音事件来识别声音场景是有效的,为今后将这一策略与以前的策略相结合的研究铺平了道路。
URL
https://arxiv.org/abs/1904.05204