论文标题
用于声学场景分类的低复杂性CNN
Low-complexity CNNs for Acoustic Scene Classification
论文作者
论文摘要
该技术报告描述了surreyaudioteam22s Dcase 2022 ASC任务1的提交，低复杂性声学场景分类（ASC）。该任务有两个规则，（a）ASC框架应具有最大128K参数，并且（b）每个推理最多应有3000万次多功能操作（MAC）。在本报告中，我们为ASC提供了遵循该任务的规则的ASC的低复杂系统。
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K parameters, and (b) there should be a maximum of 30 millions multiply-accumulate operations (MACs) per inference. In this report, we present low-complexity systems for ASC that follow the rules intended for the task.
