11 accepted papers
Deep neural network-based speech enhancement methods have become widespread, with one of its fundamental aspects being the effective extraction and application of features in the time-frequency domain. This paper proposes a hybrid feature global attention network (HFGANet) designed to efficiently ex…
The singing voice synthesis system is designed to generate realistic and expressive singing based on a given musical score. Generative Adversarial Networks (GANs) or diffusion models generate acoustic features, such as Mel-spectrograms, which are subsequently reconstructed into waveforms by a vocode…
"In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event cameras output sparse and asynchronous events, providing a potential solution to solve these problems. However, effecti…
The time-frequency domain remains central to the speech signal analysis. Enhancing the efficacy of neural network-based speech models demands a detailed multi-scale analysis of time-frequency features. This study presents the Hybrid Attention Time-Frequency Analysis Network (HATFANet), an innovative…
Time-frequency analysis in single-channel speech enhancement has received considerable attention. While Transformer-based architectures are gaining traction, their computational burden can be substantial, especially when dealing with longer speech samples. To address this, our research introduces th…
During communication, volume changes, noise, and reverberation can disturb speech signals, significantly affecting the quality and intelligibility of speech. In the context of the ICASSP 2023 Signal Processing Grand Challenge, the first Speech Signal Improvement Grand Challenge (SIG) is organized to…
In denoising and de-reverberation tasks, the dominant methods are complex spectral masking and complex spectral mapping. To combine advantages and improve speech enhancement performance, we propose a two-stage UNet (TSUNet) to estimate complex spectral masking and complex spectral mapping. We use a…
In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement. However, due to the limitation of training data and computational complexity, real-time enhancement of full-band (48 kHz) speech signals is still very challenging. Becaus…
A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions. Learning to estimate the importance of each object on the driver's real-time decision-making may help better understand human driving behavior and lead to mor…
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest from rich behavior data. Existing user behavior modeling algorithms develop different attention mechanisms to emphasize…
Due to the foveated nature of the human vision system, people can focus their visual attention on a small region of their visual field at a time, which usually contains only a single object. Estimating this object of attention in first-person (egocentric) videos is useful for many human-centered rea…