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Tampere University
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Importance weighting can dramatically narrow the performance gap in audio classification evaluations, even with limited labeled data.
Learning sound classes across varying acoustic domains without prior data access reveals a critical gap in current incremental learning approaches.
Hebbian learning, often relegated to theory, can actually boost accuracy and stability in incremental audio classification tasks by selectively tuning network kernels.
