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Qi Lian

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AAAI Conference 2025 Conference Paper

Cauchy Diffusion: A Heavy-tailed Denoising Diffusion Probabilistic Model for Speech Synthesis

Qi Lian
Yu Qi
Yueming Wang

Denoising diffusion probabilistic models (DDPMs) have gained popularity in devising neural vocoders and obtained outstanding performance. However, existing DDPM-based neural vocoders struggle to handle the prosody diversities due to their susceptibility to mode-collapse issues confronted with imbalanced data. We introduced Cauchy Diffusion, a model incorporating the Cauchy noises to address this challenge. The heavy-tailed Cauchy distribution exhibits better resilience to imbalanced speech data, potentially improving prosody modeling. Our experiments on the LJSpeech and VCTK datasets demonstrate that Cauchy Diffusion achieved state-of-the-art speech synthesis performance. Compared to existing neural vocoders, our Cauchy Diffusion notably improved speech diversity while maintaining superior speech quality. Remarkably, Cauchy Diffusion surpassed neural vocoders based on generative adversarial networks (GANs) that are explicitly optimized to improve diversity.

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JBHI Journal 2025 Journal Article

Dynamic Instance-Level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction

Qi Lian
Yueming Wang
Yu Qi

Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN’s capability to learn interpretable and diverse connections.

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