"Acoustical Intelligence in the Wild: Advancing Acoustic Signal Understanding in Open Environments"
DETAILS
Call for Papers-"Acoustical Intelligence in the Wild: Advancing Acoustic Signal Understanding in Open Environments"
Journal: Computer Speech & Language
Publisher: Elsevier
Submission Deadline: 31 May 2027
Submission Portal | Article Type | Author Guidelines |
|---|---|---|
"VSI: Acoustical Intelligence in the Wild" |
Key Requirements:
Continual/Life-long Learning for dynamic acoustic environments
Transition from static inference to dynamic adaptation
Edge deployment, privacy-preserving solutions
Overview
State-of-the-art speech/audio models fail in real-world dynamic environments. Seeks solutions for "living" acoustic systems that incrementally learn new patterns (OOD data, novel sounds, non-stationary noise) without catastrophic forgetting—critical for voice assistants, environmental monitoring, adaptive hearing aids.
Key Research Themes
Class-Incremental Learning: New sound events/spoken languages
Online Domain Adaptation: Speech enhancement in non-stationary noise
Catastrophic Forgetting Mitigation: Large-scale audio pre-trained models
Open-set Detection: Active learning for unlabeled acoustic streams
Memory-efficient CL: Edge audio devices
Privacy-preserving LL: Personalized speech interfaces
Submission Details
Opens: 15 April 2026
Closes: 31 May 2027
MANDATORY: Select "VSI: Acoustical Intelligence in the Wild"
Acceptance: 30 June 2027
Guest Editor Team
Prof. Kele Xu (Executive), National University of Defense Technology, China (kele.xu@ieee.org)
Dr. Boqing Zhu, National University of Defense Technology, China (zhuboqing@outlook.com)
Prof. Qian Kun, Beijing Institute of Technology, China (qian@bit.edu.cn)
Prof. Zixing Zhang, Hunan University, China (zixingzhang@hnu.edu.cn)
Why This Issue Matters
Bridges continual ML + audio processing gap. Enables acoustic systems that evolve with real-world soundscapes vs. static lab models. Foundation for next-gen always-learning audio AI.
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