Domain Adaptation Techniques Evaluated for Acoustic Scene Classification

This paper investigates domain adaptation techniques for acoustic scene classification, focusing on convolutional neural network (CNN) and transformer-based feature representations. The study evaluates two methods, Domain Adversarial Neural Network (DANN) and Conditional Domain Adversarial Network (CDAN), across various domain shifts. Results show DANN is consistently effective for both feature extractors, while CDAN performs well only with CNNs, suggesting that domain adaptation strategies should be tailored to the specific feature representation used. AI

IMPACT Provides insights into tailoring domain adaptation methods for specific feature representations in audio processing tasks.

RANK_REASON Academic paper on domain adaptation techniques for acoustic scene classification. [lever_c_demoted from research: ic=1 ai=1.0]

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• Conditional Domain Adversarial Network
• convolutional neural network
• DANN
• DCASE 2020
• Domain Adaptation

• paper
• other

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