Low-complexity CNNs for Acoustic Scene Classification

Arshdeep Singh; James A King; Mark D. Plumbley; Wenwu Wang; Xubo Liu

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Low-complexity CNNs for Acoustic Scene Classification

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arxiv 2208.01555 v1 pith:DLJBLNSM submitted 2022-08-02 eess.AS cs.LGcs.SD

Low-complexity CNNs for Acoustic Scene Classification

Arshdeep Singh , James A King , Xubo Liu , Wenwu Wang , Mark D. Plumbley This is my paper

classification eess.AS cs.LGcs.SD

keywords low-complexitytaskacousticclassificationmaximumreportrulesscene

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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.

Low-complexity CNNs for Acoustic Scene Classification

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