arXiv subjects

Shaokai Li

Publications and source records attributed to Shaokai Li.

5 recordsLinked to original sources

arXiv2026

Noisy Environment Adaptation of Neural Speech Codec via Focal Mask and Noise Feature Separation

Neural speech codec has attracted extensive attention for high-quality reconstruction at low-bitrate. However, real-world noise severely degrades its performance and hinders high-quality clean speech reconstruction. To tackle this problem, we propose FocalSE, a novel speech enhancement method that performs feature denoising, noise feature separation and noise recognition in the continuous embedding space of neural speech codecs. Specifically, we develop focal modulation-based compression and decompression to capture global context and local mutual information, and generate focal masks to recover clean feature embeddings. We then separate noise embeddings from noisy embeddings to improve denoising performance. Finally, we use ResNet1D-18 to recognize noise categories for better separation effectiveness. Extensive experiments on two standard datasets, LibriTTS and ESC50, demonstrate that our method outperforms state-of-the-art approaches under low-bitrate and low-SNR conditions.

eess.AS↗

arXiv2026

Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers

Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health monitoring in the Internet of Things (IoT) era. However, existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments due to static background overfitting and Non-Line-of-Sight (NLoS) signal attenuation. To address these critical bottlenecks, we propose a robust, domain-generalizable framework featuring a novel Attention-Enhanced CNN-Transformer hybrid architecture. First, we design a physics-driven \textbf{Dynamic Variance Gate (DVG)} to dynamically calculate local temporal variance, acting as a soft-attention mask that eliminates static environmental DC components while amplifying dynamic human motion. Second, we introduce a Physics-Aware Data Augmentation strategy to force the network to learn invariant morphological signatures rather than environment-specific noise. Furthermore, a Convolutional Block Attention Module (CBAM) is integrated to refine spatiotemporal features prior to Transformer-based sequence modeling. Extensive cross-domain evaluations across four distinct indoor environments demonstrate that our method achieves 97.6\% accuracy in NLoS scenarios and 98.8\% in completely unseen environments without target-domain fine-tuning. Finally, we deploy the proposed framework on an edge computing system equipped with commercial WiFi NICs. Real-world live inference field tests confirm the system's robustness against unseen environmental layouts and its capability for continuous, low-latency whole-home safety monitoring.

eess.SP↗

arXiv2024

Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network

In this paper, we propose a novel deep inductive transfer learning framework, named feature distribution adaptation network, to tackle the challenging multi-modal speech emotion recognition problem. Our method aims to use deep transfer learning strategies to align visual and audio feature distributions to obtain consistent representation of emotion, thereby improving the performance of speech emotion recognition. In our model, the pre-trained ResNet-34 is utilized for feature extraction for facial expression images and acoustic Mel spectrograms, respectively. Then, the cross-attention mechanism is introduced to model the intrinsic similarity relationships of multi-modal features. Finally, the multi-modal feature distribution adaptation is performed efficiently with feed-forward network, which is extended using the local maximum mean discrepancy loss. Experiments are carried out on two benchmark datasets, and the results demonstrate that our model can achieve excellent performance compared with existing ones.

cs.CV↗

arXiv2024

Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework

Multimodal emotion recognition systems rely heavily on the full availability of modalities, suffering significant performance declines when modal data is incomplete. To tackle this issue, we present the Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) framework, an innovative approach that sequentially engages in cross-modal alignment, reconstruction, and refinement phases to handle missing modalities and enhance emotion recognition. This framework utilizes unsupervised distribution-based contrastive learning to align heterogeneous modal distributions, reducing discrepancies and modeling semantic uncertainty effectively. The reconstruction phase applies normalizing flow models to transform these aligned distributions and recover missing modalities. The refinement phase employs supervised point-based contrastive learning to disrupt semantic correlations and accentuate emotional traits, thereby enriching the affective content of the reconstructed representations. Extensive experiments on the IEMOCAP and MSP-IMPROV datasets confirm the superior performance of CM-ARR under conditions of both missing and complete modalities. Notably, averaged across six scenarios of missing modalities, CM-ARR achieves absolute improvements of 2.11% in WAR and 2.12% in UAR on the IEMOCAP dataset, and 1.71% and 1.96% in WAR and UAR, respectively, on the MSP-IMPROV dataset.

cs.MM↗

arXiv2022

A Firefly Algorithm-based Spectral Fitting Technique for Wavelength Modulation Spectroscopy Systems

This paper proposes a novel calibration-free wavelength modulated spectroscopy (WMS) spectral fitting technique based on the firefly algorithm. The technique by simulating the information interaction behavior between fireflies to achieve the retrieval of gas concentration and laser parameters. Contrasted with the spectral fitting technique based on the classical Levenberg-Marquardt (LM) algorithm, the retrieval of gas concentrations by this technique is weakly dependent on the pre-characterization of the laser parameters. We select the P(13) absorption line of C2H2 at 1532.82 nm as the target spectra and compare the performance of two optimization method (LM and firefly) on gas concentration and laser parameters retrieval by simulation. The simulation results show that the spectral fitting technique based on the firefly algorithm performs better in terms of convergence speed and fitting accuracy, especially in the multi-parameter model without exact characterization.

physics.ins-det↗