11 accepted papers
The rapid advancement of generative AI has made audio deepfakes increasingly indistinguishable from authentic human vocals, posing significant threats to persons-of-interest (POI) such as public figures. Current detection systems primarily rely on generic, black-box models that fail to capture speak
Audio-Visual Speech Recognition (AVSR) has achieved remarkable progress in offline conditions, yet its robustness in real-world video conferencing (VC) remains largely unexplored. This paper presents the first systematic evaluation of state-of-the-art AVSR models across mainstream VC platforms, reve
Existing end-to-end neural codecs have made great progress in preserving audio quality. Despite their success, they still face challenges in achieving accurate and efficient quantization. Specifically, these codecs often overlook which features have a greater impact on perceptual audio quality durin…
Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes…
Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additi…
This paper explores the detection of frame-wise instances of violence in both audio and visual modalities, where only clip-level labels are available. Previous works selected fixed value of frames for objective optimization to model frame-level features, and applied straightforward fusion strategy t…
Most audio tagging models are trained with one-hot labels as supervised information. However, one-hot labels treat all sound events equally, ignoring the semantic hierarchy and proximity relationships between sound events. In contrast, the event descriptions contains richer information, describing t…
With the rapid development of audio deepfake technology, the credibility and authenticity of public opinion is facing a formidable challenge. Since vocoder is the key component of audio deepfake and leaves distinctive fingerprint features, we propose VFD-Net (Vocoder Fingerprints Detection Net), a n…
Mixup proves to be an efficient data augmentation method on audio classification tasks. Original mixup scheme directly mixes the waveform of two random samples, which not only ignores the temporal distribution of the sound events but may also interfere with the original sound events in another sampl…
Existing automatic sleep staging algorithms rely on accurately labeled data. However, due to the subjectivity of sleep experts, accurate labels must be obtained through joint labeling by multiple experts, which results in high time and labor costs. In this work, we treat labels mislabeled by a singl…
AAC(Advanced Audio Coding) is the most popular audio compression standard and used widely in recent years. The steganography schemes of AAC emerged gradually. This paper presents a novel steganalysis method to attack the steganography of Huffman codebook, which hide information by modifying the code…