# Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices

arXiv: https://arxiv.org/abs/2203.12314 · HTML: https://mlsanity.com/paper/2203.12314/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications](https://mlsanity.com/paper/1511.06530/) (2015)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Learning from Between-class Examples for Deep Sound Recognition](https://mlsanity.com/paper/1711.10282/) (2017)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1805.07319/) (2018)
- [SubSpectralNet - Using Sub-Spectrogram based Convolutional Neural Networks for Acoustic Scene Classification](https://mlsanity.com/paper/1810.12642/) (2018)
- [Robust Acoustic Scene Classification using a Multi-Spectrogram Encoder-Decoder Framework](https://mlsanity.com/paper/2002.04502/) (2020)
- [Multi-view Audio and Music Classification](https://mlsanity.com/paper/2103.02420/) (2021)
- [Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events](https://mlsanity.com/paper/1904.05204/) (2019)
- [Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Features](https://mlsanity.com/paper/2005.12779/) (2020)
- [An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification](https://mlsanity.com/paper/2112.09172/) (2021)
- [Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping](https://mlsanity.com/paper/2011.02955/) (2020)
- [Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification](https://mlsanity.com/paper/2107.09268/) (2021)

## Built on by (most cited first, 7 of 7)

- [Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models](https://mlsanity.com/paper/2407.01777/) (2024)
- [An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies](https://mlsanity.com/paper/2303.04104/) (2023)
- [Low-complexity deep learning frameworks for acoustic scene classification](https://mlsanity.com/paper/2206.06057/) (2022)
- [DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations](https://mlsanity.com/paper/2206.08007/) (2022)
- [Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms](https://mlsanity.com/paper/2305.09463/) (2023)
- [A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)](https://mlsanity.com/paper/2407.03110/) (2024)
- [Environmental Sound Deepfake Detection Using Deep-Learning Framework](https://mlsanity.com/paper/2604.19652/) (2026)

## Abstract (arXiv)

In this paper, we present a robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording. We first construct an ASC baseline system in which a novel inception-residual-based network architecture is proposed to deal with the mismatched recording device issue. To further improve the performance but still satisfy the low complexity model, we apply two techniques: ensemble of multiple spectrograms and channel reduction on the ASC baseline system. By conducting extensive experiments on the benchmark DCASE 2020 Task 1A Development dataset, we achieve the best model performing an accuracy of 69.9% and a low complexity of 2.4M trainable parameters, which is competitive to the state-of-the-art ASC systems and potential for real-life applications on edge devices.