arXiv subjects

Riccardo Casciotti

Publications and source records attributed to Riccardo Casciotti.

3 recordsLinked to original sources

arXiv2026

Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task

This paper presents the Domain-Agnostic Incremental Learning for Audio Classification Task of the DCASE 2026 Challenge. Incremental learning refers to sequentially learning new tasks with the same system while maintaining its knowledge and performance on the previously learned task. Domain-incremental learning for sound classification refers to learning the same sound classes but in different acoustic domains, and was formalized as a data challenge for the first time in DCASE 2026. The task required participants to train a system to learn ten sound classes in three different domains, starting from a baseline system trained on the first domain. At each incremental task, learning had to be done without access to previous task data. The systems were ranked by the overall average accuracy calculated over the three domains. The task generated high interest and received submissions from 21 teams, with most submissions outperforming the provided baseline system. The highest performance of 79.6% was obtained using a combination of continual learning strategies that resulted in a balanced accuracy across domains, hence a good balance of retaining old and learning new information.

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arXiv2026

Investigating catastrophic forgetting in sound event classification

This work investigates a number of approaches to prevent catastrophic forgetting in class incremental learning scenarios for sound event classification tasks. We analyze the problem using architectural and regularization approaches, using FSD50K and AudioSet datasets. We design incremental stages and solutions that selectively protect the kernels of the network from weight updates to prevent catastrophic forgetting, and a dynamic head solution that expands itself each time a new task is learned. The findings show that catastrophic forgetting mainly happens in deeper layers, in particular in the classifier head. For the studied in-domain sound classification problem, the solution that seems to alleviate catastrophic forgetting and is the most efficient is a full freezing of the feature extractor with a fine-tuning of the dynamic head classifier, showing little to no forgetting and great training stability, and a good balance between memory-stability and learning plasticity.

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arXiv2026

Incremental learning for audio classification with Hebbian Deep Neural Networks

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.

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