This paper proposes to use both audio input and subject information to predict the personalized preference of two audio segments with the same content in different qualities. A siamese network is used to compare the inputs and predict the preference. Several different structures for each side of the siamese network are investigated, and an LDNet with PANNs' CNN6 as the encoder and a multi-layer perceptron block as the decoder outperforms a baseline model using only audio input the most, where the overall accuracy grows from 77.56% to 78.04%. Experimental results also show that using all the subject information, including age, gender, and the specifications of headphones or earphones, is more effective than using only a part of them.
Deep Learning for Personalized Binaural Audio Reproduction
Lab of Artificial Intelligence for Education, East China Normal University（人工智能教育实验室，华东师范大学）；Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University（智能信息处理重点实验室，深圳大学电子与信息工程学院）；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Electronics and Communication Engineering, Guangzhou University（电子与通信工程学院，广州大学）；School of Computer Science and Technology, East China Normal University（计算机科学与技术学院，华东师范大学）；Medizinische Physik, Carl von Ossietzky University of Oldenburg（医学物理，奥尔登堡大学）；Institute of Acoustics, Chinese Academy of Sciences（声学研究所，中国科学院）
Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation
Renmin University of China（中国人民大学）；XREAL；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京高精尖创新中心）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Tsinghua University（清华大学）
MultiSoundGen: Video-to-Audio Generation for Multi-Event Scenarios via SlowFast Contrastive Audio-Visual Pretraining and Direct Preference Optimization
Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation
Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）；Baidu（百度）
