Device Invariance using Domain Adaptation on Acoustic Scene Classification

• URL: http://arxiv.org/abs/2607.25887v1
• Date: Tue, 28 Jul 2026 15:47:37 GMT
• Title: Device Invariance using Domain Adaptation on Acoustic Scene Classification
• Abstract summary: Two well-known domain adaptation techniques, namely domain adversarial neural network (also called DANN) and conditional domain adversarial network (also called CDAN) are evaluated.<n>DANN provides effective domain adaptation fairly consistently for both feature extractors.<n>On the other hand, CDAN provides effective domain adaptation only for CNN-based feature extractors.
• Score: 1.8368732715925258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based feature representations for acoustic scene classification. Two well-known domain adaptation techniques, namely domain adversarial neural network (also called DANN) and conditional domain adversarial network (also called CDAN) are evaluated under various domain shifts. Our study indicates that DANN provides effective domain adaptation fairly consistently for both feature extractors. On the other hand, CDAN provides effective domain adaptation only for CNN-based feature extractors. The study gives insights into how domain adaptation methods may need to be tailored to the underlying feature representation. Experimental evaluation with multiple devices on the DCASE 2020 dataset supports the observations.

Related papers

• Vision Transformer-based Adversarial Domain Adaptation [5.611768906855499]
  Vision transformer (ViT) has attracted tremendous attention since its emergence and has been widely used in various computer vision tasks. In this paper, we fill this gap by employing the ViT as the feature extractor in adversarial domain adaptation. We empirically demonstrate that ViT can be a plug-and-play component in adversarial domain adaptation.
  arXiv  Detail & Related papers  (2024-04-24T11:41:28Z)
• Towards Subject Agnostic Affective Emotion Recognition [8.142798657174332]
  EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs) We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our proposed approach is shown to be effective in experiments on a public aBICs dataset.
  arXiv  Detail & Related papers  (2023-10-20T23:44:34Z)
• Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation [65.78246406460305]
  compositional semantic mixing represents the first unsupervised domain adaptation technique for point cloud segmentation. We present a two-branch symmetric network architecture capable of concurrently processing point clouds from a source domain (e.g. synthetic) and point clouds from a target domain (e.g. real-world)
  arXiv  Detail & Related papers  (2023-08-28T14:43:36Z)
• Cyclically Disentangled Feature Translation for Face Anti-spoofing [61.70377630461084]
  We propose a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN) CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels.
  arXiv  Detail & Related papers  (2022-12-07T14:12:34Z)
• Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection [8.583307102907295]
  Object recognition from images means to automatically find object(s) of interest and to return their category and location information. Benefiting from research on deep learning, like convolutional neural networks(CNNs) and generative adversarial networks, the performance in this field has been improved significantly. mismatching distributions, i.e., domain shifts, lead to a significant performance drop.
  arXiv  Detail & Related papers  (2022-02-12T00:21:27Z)
• Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection [107.52026281057343]
  We introduce a Frequency Spectrum Augmentation Consistency (FSAC) framework with four different low-frequency filter operations. In the first stage, we utilize all the original and augmented source data to train an object detector. In the second stage, augmented source and target data with pseudo labels are adopted to perform the self-training for prediction consistency.
  arXiv  Detail & Related papers  (2021-12-16T04:07:01Z)
• Topic Driven Adaptive Network for Cross-Domain Sentiment Classification [6.196375060616161]
  We propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: semantics attention network and domain-specific word attention network. Experiments validate the effectiveness of our TDAN on sentiment classification across domains.
  arXiv  Detail & Related papers  (2021-11-28T10:17:11Z)
• TDACNN: Target-domain-free Domain Adaptation Convolutional Neural Network for Drift Compensation in Gas Sensors [6.451060076703026]
  In this paper, deep learning based on a target-domain-free domain adaptation convolutional neural network (TDACNN) is proposed. The main concept is that CNNs extract not only the domain-specific features of samples but also the domain-invariant features underlying both the source and target domains. Experiments on two datasets drift under different settings demonstrate the superiority of TDACNN compared with several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-14T16:30:17Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation [66.74638960925854]
  Partial domain adaptation (PDA) deals with a realistic and challenging problem when the source domain label space substitutes the target domain. We propose an Adaptively-Accumulated Knowledge Transfer framework (A$2$KT) to align the relevant categories across two domains.
  arXiv  Detail & Related papers  (2020-08-27T00:53:43Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.