Yuan Z, Wu D, Wu X, Qu T.
Sound event localization and detection based on iterative separation in embedding space, in
2023 6th International Conference on Information Communication and Signal Processing (ICICSP). Xian, China; 2023:455-459.
Ge Z, Tian P, Li L, Qu T.
Rendering Near-field Point Sound Sources Through an Iterative Weighted Crosstalk Cancellation Method, in
Audio Engineering Society Convention 154. Helsinki, Finland; 2023:10649.
Wang Y, Lan Z, Wu X, Qu T.
TT-Net: Dual-Path Transformer Based Sound Field Translation in the Spherical Harmonic Domain, in
International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island, Greece; 2023:1-5.
曲天书, 吴玺宏, 王奕文.; 2023.
一种基于双路自注意力机制学习的多点采样声场重建方法. China patent CN 202310667120.6.
曲天书, 吴玺宏, 葛钟书.; 2023.
一种基于扬声器阵列的近场声源重放方法. China patent CN 202310532598.8.
曲天书, 吴玺宏, 吴东航.; 2023.
一种基于深度学习和柱谐分解的空间主动降噪方法. China patent CN 202310955389.4.
Gao S, Wu X, Qu T.
A Physical Model-Based Self-Supervised Learning Method for Signal Enhancement Under Reverberant Environment. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2023;31:2100-2110.
AbstractIn a reverberant environment, interferences such as reflections and background noise can degrade the perception of the sound source signal. Although the DNN-based methods have made a tremendous breakthrough in addressing this issue, the performance of these models is highly dependent on the completeness of the training dataset, which will limit its generalization under unknown environments. In this article, we propose a physical model-based self-supervised learning (PMSSL) method to realize the DNN model optimization under unknown scenarios. This method incorporates a room reverberation physical model into the sound source enhancement model optimization process, realizing the self-learning of the DNN model under physical constraints. In this process, the time-frequency characteristics of the input signal and the spatial feature of the reverberation environment are utilized for parameter optimization, improving the adaptability of the DNN model under unknown scenarios. Experimental results based on simulated and measured data prove that the proposed method can obtain much more accurate source signal enhancement results compared with the pre-trained models, verifying its effectiveness and adaptability in new environments.