# Continual Learning For On-Device Environmental Sound Classification

arXiv: https://arxiv.org/abs/2207.07429 · HTML: https://mlsanity.com/paper/2207.07429/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Rainbow Memory: Continual Learning with a Memory of Diverse Samples](https://mlsanity.com/paper/2103.17230/) (2021)
- [Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines](https://mlsanity.com/paper/1810.12488/) (2018)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [Temporal Knowledge Distillation for On-device Audio Classification](https://mlsanity.com/paper/2110.14131/) (2021)
- [Continual Learning of New Sound Classes using Generative Replay](https://mlsanity.com/paper/1906.00654/) (2019)
- [A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification](https://mlsanity.com/paper/2203.15751/) (2022)
- [Low-complexity acoustic scene classification in DCASE 2022 Challenge](https://mlsanity.com/paper/2206.03835/) (2022)
- [QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design](https://mlsanity.com/paper/2206.13909/) (2022)
- [A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning](https://mlsanity.com/paper/2102.03012/) (2021)
- [ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systems](https://mlsanity.com/paper/2106.03122/) (2021)
- [Progressive Continual Learning for Spoken Keyword Spotting](https://mlsanity.com/paper/2201.12546/) (2022)
- [Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting](https://mlsanity.com/paper/2203.16361/) (2022)
- [Deep Neural Decision Forest for Acoustic Scene Classification](https://mlsanity.com/paper/2203.03436/) (2022)

## Built on by (most cited first, 4 of 4)

- [Separate Anything You Describe](https://mlsanity.com/paper/2308.05037/) (2023)
- [Simple Pooling Front-ends For Efficient Audio Classification](https://mlsanity.com/paper/2210.00943/) (2022)
- [Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning](https://mlsanity.com/paper/2406.18313/) (2024)
- [Continual Adaptation for Pacific Indigenous Speech Recognition](https://mlsanity.com/paper/2603.06310/) (2026)

## Abstract (arXiv)

Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation resources (e.g., model size, running memory). To address this issue, we propose a simple and efficient continual learning method. Our method selects the historical data for the training by measuring the per-sample classification uncertainty. Specifically, we measure the uncertainty by observing how the classification probability of data fluctuates against the parallel perturbations added to the classifier embedding. In this way, the computation cost can be significantly reduced compared with adding perturbation to the raw data. Experimental results on the DCASE 2019 Task 1 and ESC-50 dataset show that our proposed method outperforms baseline continual learning methods on classification accuracy and computational efficiency, indicating our method can efficiently and incrementally learn new classes without the catastrophic forgetting problem for on-device environmental sound classification.