LLAM
The Lab of Large Audio Model (LLAM) is committed to exploring and advancing the forefront and future of audio and sound technology, and building large audio models.
Recent News
[21/12/2024] $\bullet$ We are pleased to announce that five papers from our research group have been accepted to ICASSP 2025, one of the most prestigious conferences in the field of audio, speech, and signal processing. The accepted papers cover a wide array of cutting-edge topics in machine learning, speech processing, computer vision, and multimodal learning. Here are the titles of our accepted papers: CycleFlow: Leveraging Cycle Consistency in Flow Matching for Speaker Style Adaptation,Homogeneous Graph Extraction: An Approach to Learning Heterogeneous Graph Embedding,Graph Contrastive Learning with Decoupled Augmentation,VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection,PointActionCLIP: Preventing Transfer Degradation in Point Cloud Action Recognition with a Triple-Path CLIP.
[10/12/2024] $\bullet$ Our team is proud to announce that two of our research papers have been accepted at the prestigious AAAI Conference on Artificial Intelligence (AAAI 2025), a leading international conference in the field of AI, recognized as a CCF-A class. The first accepted paper, titled “ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression,” introduces a novel approach to improve the performance of deep regression models. This method enhances data efficiency and effectiveness, particularly in imbalanced datasets, by adjusting the cosine distance between anchor and negative samples within a contrastive learning framework. The second paper, “RUNA: Object-level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations,” tackles the challenge of enabling object detectors to recognize unknown objects. RUNA leverages a dual encoder architecture and a regional uncertainty alignment mechanism to effectively distinguish between in-distribution and out-of-distribution objects, significantly outperforming existing methods.
[13/11/2024] $\bullet$ We are thrilled to announce that the paper “Cocktait: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference,” co-authored with Professor Jiguang Wan’s team from Huazhong University of Science and Technology, has been accepted to DATE 2025! This research, which focuses on accelerating large model inference, is a significant step towards enabling more efficient deployment of LLMs with extended context windows. The paper, authored by Wei Tao, Bin Zhang, Xiaoyang Qu, Jiguang Wan, and Jianzong Wang (corresponding author), will be presented at the Design, Automation and Test in Europe Conference in 2025. This collaboration exemplifies our commitment to advancing AI and machine learning technologies through partnerships with leading research institutions.
[15/10/2024] $\bullet$ One research paper from our team has been accepted for presentation at the 26th International Conference on High Performance Computing and Communications (HPCC2024). The accepted paper is titled “Incremental Label Distribution Learning With Scalable Graph Convolutional Networks.” HPCC2024 is a leading forum for advances in high-performance computing and communications technology. Sponsored by IEEE, the conference brings together experts from academia, industry, and government to address challenges and present innovations in theoretical foundations, systems, infrastructure, tools, and applications. This year’s event continues the conference’s tradition of showcasing cutting-edge research and defining future directions in the rapidly evolving field of high-performance computing. Our team’s participation underscores our commitment to pushing the boundaries of this critical technology.
[20/09/2024] $\bullet$ We are thrilled to announce that our paper, “IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding,” has been officially accepted for presentation at the prestigious EMNLP 2024 main conference! IDEAW represents a significant advancement in neural audio watermarking, and we are excited to share our findings with the NLP community at this premier event.
Research Direction
Large Audio Model
Research on Large Audio Models aims to advance the field of audio processing, generation, understanding, and multimodal processing, with the goal of enabling new and innovative applications in areas such as speech recognition, virtual assistants, music composition, audio synthesis, and more.
Text to Speech
Research on high-quality audio, few-shot TTS, low resource TTS, and expressive TTS is mainly applied to scenarios such as speech interaction, information broadcasting, and text-to-speech reading, as well as in intelligent voice outbound calls and intelligent agents.
Voice Conversion
Research that aims to transform the vocal characteristics of a speaker while preserving the linguistic content of their speech. It has various applications in speech processing, including speaker adaptation, voice disguise, and emotion transfer.
Speech Security
Research aims to address various security threats and vulnerabilities associated with speech data, speech recognition systems, and voice communication.
Music AI
Research topics related to music information retrieval, including song detection, singer identification, main melody extraction, and voice beautification.
