Meta-ensemble learning on diverse ICBHI data splits reaches 66.49% Score and improves generalization on two external datasets.
Masked modeling duo: Towards a universal audio pre-training frame- work
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ULTRAS unifies audio and speech representation learning in a single transformer by applying patch masking to log-mel spectrograms and using a joint spectral-temporal prediction loss.
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Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
Meta-ensemble learning on diverse ICBHI data splits reaches 66.49% Score and improves generalization on two external datasets.
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ULTRAS -- Unified Learning of Transformer Representations for Audio and Speech Signals
ULTRAS unifies audio and speech representation learning in a single transformer by applying patch masking to log-mel spectrograms and using a joint spectral-temporal prediction loss.