Acoustic Scene Classification with Squeeze-Excitation Residual Networks

by   Javier Naranjo-Alcazar, et al.

Acoustic scene classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene location (e. g. park, airport, etc.). Many state-of-the-art solutions to ASC incorporate data augmentation techniques and model ensembles. However, considerable improvements can also be achieved only by modifying the architecture of convolutional neural networks (CNNs). In this work we propose two novel squeeze-excitation blocks to improve the accuracy of a CNN-based ASC framework based on residual learning. The main idea of squeeze-excitation blocks is to learn spatial and channel-wise feature maps independently instead of jointly as standard CNNs do. This is usually achieved by some global grouping operators, linear operators and a final calibration between the input of the block and its obtained relationships. The behavior of the block that implements such operators and, therefore, the entire neural network, can be modified depending on the input to the block, the established residual configurations and the selected non-linear activations. The analysis has been carried out using the TAU Urban Acoustic Scenes 2019 dataset ( presented in the 2019 edition of the DCASE challenge. All configurations discussed in this document exceed the performance of the baseline proposed by the DCASE organization by 13% percentage points. In turn, the novel configurations proposed in this paper outperform the residual configurations proposed in previous works.


On the performance of different excitation-residual blocks for Acoustic Scene Classification

Acoustic Scene Classification (ASC) is a problem related to the field of...

Sound Event Localization and Detection using Squeeze-Excitation Residual CNNs

Sound Event Localization and Detection (SELD) is a problem related to th...

DCASE 2019: CNN depth analysis with different channel inputs for Acoustic Scene Classification

The objective of this technical report is to describe the framework used...

A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification

In Acoustic Scene Classification (ASC) two major approaches have been fo...

Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization

It is a practical research topic how to deal with multi-device audio inp...

QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design

This technical report describes the details of our TASK1A submission of ...

Please sign up or login with your details

Forgot password? Click here to reset