Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint

TL;DR

This paper presents a low-complexity convolutional neural network with squeeze-excitation for acoustic scene classification, effectively handling device mismatch and outperforming baseline models by 17 percentage points.

Contribution

Introduces a two-step system using Gamamtone filter bank and squeeze-excitation CNN for device-mismatch ASC with low complexity.

Findings

Outperforms baseline by 17 percentage points

Effective handling of mismatch devices

Low-complexity model suitable for real-world applications

Abstract

Acoustic scene classification (ASC) is one of the most popular problems in the field of machine listening. The objective of this problem is to classify an audio clip into one of the predefined scenes using only the audio data. This problem has considerably progressed over the years in the different editions of DCASE. It usually has several subtasks that allow to tackle this problem with different approaches. The subtask presented in this report corresponds to a ASC problem that is constrained by the complexity of the model as well as having audio recorded from different devices, known as mismatch devices (real and simulated). The work presented in this report follows the research line carried out by the team in previous years. Specifically, a system based on two steps is proposed: a two-dimensional representation of the audio using the Gamamtone filter bank and a convolutional neural network using squeeze-excitation techniques. The presented system outperforms the baseline by about 17 percentage points.