论文解读
Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers
生物声学 | 8.0/10
生物声学
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Estimating Respiratory Effort from Nocturnal Breathing Sounds for Obstructive Sleep Apnoea Screening
音频分类 | 6.5/10
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Fusion of Multimodal Estimations by Extended State Hidden Markov Model: Application to Fetal Heart Rate Monitoring
生物声学 | 7.0/10
会议任务专题
ICASSP 2026 - 生物声学
共 12 篇 ICASSP 2026 生物声学 方向论文
论文解读
Identifying Birdsong Syllables without Labelled Data
生物声学 | 7.0/10
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Learning Domain-Robust Bioacoustic Representations for Mosquito Species Classification with Contrastive Learning and Distribution Alignment
生物声学 | 7.5/10
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Multi-Layer Attentive Probing Improves Transfer of Audio Representations for Bioacoustics
生物声学 | 7.5/10
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Multi-Scale Physiologically-Motivated Alignment for Auditory Attention Decoding
听觉注意力解码 | 7.5/10
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Random Matrix-Driven Graph Representation Learning For Bioacoustic Recognition
生物声学 | 7.5/10
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Representation-Diverse Self-Supervision for Cross-Domain Bioacoustic Learning in Low-Resource Settings
生物声学 | 7.0/10
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Snore Sound Classification Based on Physiological Features and Adaptive Loss Function
音频分类 | 6.5/10
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Testing The Efficient Coding Hypothesis Beyond Humans: The Auditory Kernels of Bat Vocalizations
生物声学 | 7.5/10
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Transfer Learning for Paediatric Sleep Apnoea Detection using Physiology-Guided Acoustic Models
音频分类 | 7.0/10
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Unsupervised Discovery and Analysis of the Vocal Repertoires and Patterns of Select Corvid Species
生物声学 | 7.5/10
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USVexplorer: Robust Detection of Ultrasonic Vocalizations with Cross Species Generalization
音频事件检测 | 8.0/10
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Utilizing Information Theoretic Approach to Study Cochlear Neural Degeneration
生物声学 | 6.5/10
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Vib2Sound: Separation Of Multimodal Sound Sources
语音分离 | 6.5/10
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Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
音频分类 | 8.0/10
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Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection
语音伪造检测 | 6.5/10
论文解读
Centering Ecological Goals in Automated Identification of Individual Animals
这篇论文旨在解决一个关键问题：为什么近年来在动物个体自动识别（基于图像或声音）上报告的高准确率算法，却很少转化为生态学实践中的常规工具？其方法核心是提出一个“以生态目标为中心”的评估与部署框架，强调自动化识别的有用性取决于其服务的具体生态问题、可用数据以及错误类型带来的实际后果。与以往主要关注算法准