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

Autor:	Dibbo, Sayanton V., Moore, Juston S., Kenyon, Garrett T., Teti, Michael A.
Rok vydání:	2023
Předmět:	Computer Science - Sound Computer Science - Cryptography and Security Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at 2024 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops (ICASSPW)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.12882 Zobrazit plný text záznamu View this record from Arxiv