# LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition

arXiv: https://arxiv.org/abs/2308.12882 · HTML: https://mlsanity.com/paper/2308.12882/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Adversarial Attacks and Defences Competition](https://mlsanity.com/paper/1804.00097/) (2018)
- [Enabling Fast and Universal Audio Adversarial Attack Using Generative Model](https://mlsanity.com/paper/2004.12261/) (2020)
- [Revisiting Sparse Convolutional Model for Visual Recognition](https://mlsanity.com/paper/2210.12945/) (2022)
- [Deep Learning Approaches for Understanding Simple Speech Commands](https://mlsanity.com/paper/1810.02364/) (2018)
- [WITCHcraft: Efficient PGD attacks with random step size](https://mlsanity.com/paper/1911.07989/) (2019)
- [Spatial Data Augmentation with Simulated Room Impulse Responses for Sound Event Localization and Detection](https://mlsanity.com/paper/2110.06501/) (2021)

## Built on by (most cited first, 6 of 6)

- [Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures](https://mlsanity.com/paper/2403.14772/) (2024)
- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm](https://mlsanity.com/paper/2410.18794/) (2024)
- [Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?](https://mlsanity.com/paper/2511.20710/) (2025)
- [Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes](https://mlsanity.com/paper/2607.01297/) (2026)
- [Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models](https://mlsanity.com/paper/2604.16532/) (2026)

## Abstract (arXiv)

Audio classification aims at recognizing audio signals, including speech commands or sound events. However, current audio classifiers are susceptible to perturbations and adversarial attacks. In addition, real-world audio classification tasks often suffer from limited labeled data. To help bridge these gaps, previous work developed neuro-inspired convolutional neural networks (CNNs) with sparse coding via the Locally Competitive Algorithm (LCA) in the first layer (i.e., LCANets) for computer vision. LCANets learn in a combination of supervised and unsupervised learning, reducing dependency on labeled samples. Motivated by the fact that auditory cortex is also sparse, we extend LCANets to audio recognition tasks and introduce LCANets++, which are CNNs that perform sparse coding in multiple layers via LCA. We demonstrate that LCANets++ are more robust than standard CNNs and LCANets against perturbations, e.g., background noise, as well as black-box and white-box attacks, e.g., evasion and fast gradient sign (FGSM) attacks.