# Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context

arXiv: https://arxiv.org/abs/2210.08610 · HTML: https://mlsanity.com/paper/2210.08610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 86 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 30 of 35)

- [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)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [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)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [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)
- [Towards Learning a Universal Non-Semantic Representation of Speech](https://mlsanity.com/paper/2002.12764/) (2020)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (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)
- [Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps](https://mlsanity.com/paper/2002.07687/) (2020)
- [Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1805.07319/) (2018)
- [SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification](https://mlsanity.com/paper/2103.16858/) (2021)
- [SubSpectralNet - Using Sub-Spectrogram based Convolutional Neural Networks for Acoustic Scene Classification](https://mlsanity.com/paper/1810.12642/) (2018)
- [QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design](https://mlsanity.com/paper/2206.13909/) (2022)
- [A Two-Stage Approach to Device-Robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.01447/) (2020)
- [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)
- [Receptive-field-regularized CNN variants for acoustic scene classification](https://mlsanity.com/paper/1909.02859/) (2019)
- [Deep Feature Embedding and Hierarchical Classification for Audio Scene Classification](https://mlsanity.com/paper/2002.04857/) (2020)
- [An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification](https://mlsanity.com/paper/2112.09172/) (2021)
- [Acoustic scene classification using teacher-student learning with soft-labels](https://mlsanity.com/paper/1904.10135/) (2019)
- [Deep Learning Frameworks Applied For Audio-Visual Scene Classification](https://mlsanity.com/paper/2106.06840/) (2021)
- [Convolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge](https://mlsanity.com/paper/1810.04273/) (2018)
- [Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification](https://mlsanity.com/paper/1909.02869/) (2019)
- [Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping](https://mlsanity.com/paper/2011.02955/) (2020)
- [Relation-guided acoustic scene classification aided with event embeddings](https://mlsanity.com/paper/2205.00499/) (2022)

## Built on by (most cited first, 1 of 1)

- [Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms](https://mlsanity.com/paper/2305.09463/) (2023)

## Abstract (arXiv)

In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. In particular, we firstly propose an inception-based and low footprint ASC model, referred to as the ASC baseline. The proposed ASC baseline is then compared with benchmark and high-complexity network architectures of MobileNetV1, MobileNetV2, VGG16, VGG19, ResNet50V2, ResNet152V2, DenseNet121, DenseNet201, and Xception. Next, we improve the ASC baseline by proposing a novel deep neural network architecture which leverages residual-inception architectures and multiple kernels. Given the novel residual-inception (NRI) model, we further evaluate the trade off between the model complexity and the model accuracy performance. Finally, we evaluate whether sound events occurring in a sound scene recording can help to improve ASC accuracy, then indicate how a sound scene context is well presented by combining both sound scene and sound event information. We conduct extensive experiments on various ASC datasets, including Crowded Scenes, IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 1A and 1B, 2019 Task 1A and 1B, 2020 Task 1A, 2021 Task 1A, 2022 Task 1. The experimental results on several different ASC challenges highlight two main achievements; the first is to propose robust, general, and low complexity ASC systems which are suitable for real-life applications on a wide range of edge devices and mobiles; the second is to propose an effective visualization method for comprehensively presenting a sound scene context.