AudioMosaic learns general-purpose audio representations through contrastive pre-training with structured spectrogram masking, reaching state-of-the-art results on standard benchmarks and improving audio-language tasks.
Esdd 2026: Environmental sound deepfake detection challenge evalu- ation plan
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
The ESDD2 challenge evaluated 13 teams on component-level audio spoofing detection, with the top system reaching 0.8775 Macro-F1 by using modular decomposition, self-supervised encoders, and targeted augmentation.
DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.
EnvTriCascade is a tri-stage cascaded framework using mix-consistency detection followed by dual SSL-based five-class classifiers with cross-branch attention and RawBoost augmentation, achieving 0.8266 Macro-F1 on the ESDD2 2026 challenge test set.
citing papers explorer
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AudioMosaic: Contrastive Masked Audio Representation Learning
AudioMosaic learns general-purpose audio representations through contrastive pre-training with structured spectrogram masking, reaching state-of-the-art results on standard benchmarks and improving audio-language tasks.
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Overview of ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge
The ESDD2 challenge evaluated 13 teams on component-level audio spoofing detection, with the top system reaching 0.8775 Macro-F1 by using modular decomposition, self-supervised encoders, and targeted augmentation.
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DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection
DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.
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EnvTriCascade: An Environment-Aware Tri-Stage Cascaded Framework for ESDD2 2026 Challenge
EnvTriCascade is a tri-stage cascaded framework using mix-consistency detection followed by dual SSL-based five-class classifiers with cross-branch attention and RawBoost augmentation, achieving 0.8266 Macro-F1 on the ESDD2 2026 challenge test set.