10 accepted papers
Sleep apnea is a common sleep disorder that, if untreated, can lead to serious health issues. Snoring is a typical symptom of sleep apnea and can be utilized to develop a noncontact automatic detection method for sleep apnea severity classification (SASC). However, due to patient heterogeneity, the…
In applications such as virtual reality and augmented reality, binaural audio provides listeners with a more immersive experience. To synthesize binaural audio with enhanced spatial localization, especially in scenarios involving moving sound sources, accurate phase estimation is crucial. However, e…
In real-world scenarios, accent variations often reduce Automatic Speech Recognition (ASR) accuracy. Addressing this typically involves a multi-task ASR and Accent Recognition (ASR-AR) framework, but there is limited research on optimizing task-specific feature extraction and enhancing ASR with AR i…
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…
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…
Obstructive sleep apnea (OSA) is a common sleep disorder. The diagnosis of OSA based on snoring is low-cost, convenient and non-invasive. In this study, we place a microphone under the patient’s bed and combined with full-night polysomnography to record audio signals. Five machine learning models an…
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…