Incremental learning for audio classification with Hebbian Deep Neural Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911608140201984 |
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| author | Casciotti, Riccardo De Santis, Francesco Antonietti, Alberto Mesaros, Annamaria |
| author_facet | Casciotti, Riccardo De Santis, Francesco Antonietti, Alberto Mesaros, Annamaria |
| contents | The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on selected kernels to learn new information and on others to retain previous knowledge. Using the ESC-50 dataset, the proposed method achieves 76.3% overall accuracy over five incremental steps, outperforming a baseline without kernel plasticity (68.7%) and demonstrating significantly greater stability across tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18270 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Incremental learning for audio classification with Hebbian Deep Neural Networks Casciotti, Riccardo De Santis, Francesco Antonietti, Alberto Mesaros, Annamaria Audio and Speech Processing Machine Learning The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on selected kernels to learn new information and on others to retain previous knowledge. Using the ESC-50 dataset, the proposed method achieves 76.3% overall accuracy over five incremental steps, outperforming a baseline without kernel plasticity (68.7%) and demonstrating significantly greater stability across tasks. |
| title | Incremental learning for audio classification with Hebbian Deep Neural Networks |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2604.18270 |