科研成果

2024
Wu D, Wu X, Qu T. A HYBRID DEEP-ONLINE LEARNING BASED METHOD FOR ACTIVE NOISE CONTROLIN WAVE DOMAIN, in International Conference on Acoustics, Speech and Signal Processing (ICASSP). COEX, Seoul, Korea; 2024.
2023
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.Abstract
In 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.
2022
Qu T, Xu J, Yuan Z, Wu X. Higher order ambisonics compression method based onautoencoder, in Audio Engineering Society Convention 153. online; 2022:Express paper 9. 访问链接Abstract
The compression of three-dimensional sound field signals has always been a very important issue. Recently, an Independent Component Analysis (ICA) based Higher Order Ambisonics (HOA) compression method introduces blind source separation to solve the shortcomings of discontinuity between frames in the existing Singular Value Decomposition (SVD) based methods. However, ICA is weak to model the reverberant environment, and its target is not to recover original signal. In this work, we replace ICA with autoencoder to further improve the above method’s ability to cope with reverberation conditions and ensure the unanimous optimization both in separation and recovery by reconstruction loss. We constructed a dataset with simulated and recorded signals, and verified the effectiveness of our method through objective and subjective experiments.
Gao S, Wu X, Qu T. Localization of Direct Source and Early Reflections Using HOA Processing and DNN Model, in Audio Engineering Society Convention 152.; 2022:10560. 访问链接
Wang Y, Wu X, Qu T. UP-WGAN: Upscaling Ambisonic Sound Scenes Using Wasserstein Generative Adversarial Networks, in Audio Engineering Society Convention 152.; 2022:10577. 访问链接
Wang C, Wang Z, Xie B, Shi X, Yang P, Liu L, Qu T, Qin Q, Xing Y, Zhu W, et al. Binaural processing deficit and cognitive impairment in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022;28(6):1085-1099.
Chao P, Wang Y, Wu X, Qu T. A Multi-channel Speech Separation System for Unknown Number of Multiple Speakers, in 2022 5th International Conference on Information Communication and Signal Processing (ICICSP). Shenzhen, China; 2022.
曲天书, 吴玺宏, 王奕文.; 2022. 一种基于声压图学习的球谐系数升阶方法及声场描述方法. China patent CN 202210650517.X.
曲天书, 吴玺宏, 高山.; 2022. 一种基于稀疏网络模型的声场球谐函数信号频域扩展方法. China patent CN 202210231178.1.
曲天书, 吴玺宏, 高山.; 2022. 一种房间混响环境下直达声和一次反射声定向方法. China patent CN 202210233276.9.
Gao S, Lin J, Wu X, Qu T. Sparse DNN Model for Frequency Expanding of Higher Order Ambisonics Encoding Process. IEEE/ACM Transactions on Audio, Speech, and Language Processing [Internet]. 2022;30:1124-1135. 访问链接
2021
Xu J, Niu Y, Wu X, Qu T. Higher order ambisonics compression method based on independent component analysis, in Audio Engineering Society Convention 150.; 2021:10456.
Zhang M, Guan T, Chen L, Fu T, Su D, Qu T. Individualized HRTF-based Binaural Renderer for Higher-Order Ambisonics, in Audio Engineering Society Convention 150.; 2021:10454.
Chen J, Wu X, Qu T. Early Reflections Based Speech Enhancement, in 2021 4th International Conference on Information Communication and Signal Processing (ICICSP). ShangHai, China; 2021:183-187.

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