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.
[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.
[16/07/2024] $\bullet$ Today announced the acceptance of its groundbreaking research paper, “Beyond Aggregation: Efficient Federated Model Consolidation with Heterogeneity-Adaptive Weights Diffusion,” at the prestigious Conference on Information and Knowledge Management (CIKM) 2024. This innovative work addresses the critical challenge of communication costs in Federated Learning (FL), a privacy-preserving approach to training machine learning models across decentralized devices. The team pioneers the use of diffusion models, renowned for their success in AI-generated content, to revolutionize how model weights are consolidated on the server-side of FL systems. Our FedDiff method not only significantly reduces communication overhead but also demonstrates remarkable convergence speed, accuracy, and robustness against noise. This research has the potential to unlock broader real-world applications of Federated Learning in fields like healthcare, finance, and IoT. CIKM is an international forum for presenting and discussing cutting-edge research in information and knowledge management. Acceptance at CIKM underscores the significance and quality of this research contribution.
[16/05/2024] $\bullet$ It feels amazing to receive an acceptance notification from a top-tier conference on a weekday afternoon! The latest research paper “Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning,” a collaboration between Ping An Technology’s Dr. Jianzong Wang’s team and Professor Tianyi Zhou’s team from the University of Maryland, has been accepted as a long paper at ACL 2024 CCF Class A paper, with an acceptance rate of less than 20%. This represents a significant breakthrough in the field of instruction-tuning for large models. For the first time, we have revealed the consistency in instruction difficulty perception across models of different scales and achieved over a 20-fold speed improvement in the large model training process through our superfiltering method. This achievement opens up new avenues for data filtering technology. We welcome citations from our peers! Research Highlights: 1. Weak-to-Strong Data Consistency: We discovered that both small and large language models exhibit a high degree of consistency in perceiving and evaluating the difficulty of instruction-tuning data. This finding is crucial for optimizing data filtering processes. 2. Efficient Superfiltering Strategy: We proposed the first superfiltering method that uses small models (e.g., GPT-2) to select data, significantly accelerating the fine-tuning process of large language models. 3. Effectiveness of Selected Training Data: Superfiltering is highly precise in allocating high-quality and information-rich data. Models trained with only 5% of the filtered data performed similarly to or even better than models trained with the entire dataset in multiple benchmark tests. The complete research results and code are publicly available on GitHub: https://github.com/tianyi-lab/Superfiltering. This is our second paper at a top NLP conference. Our team’s collaboration with the University of Maryland has already resulted in a paper published at NAACL, addressing the innovative problem of how to automatically identify high-quality instruction data from datasets during large model training.
[09/05/2024] $\bullet$ The 2024 Twentieth International Conference on Intelligent Computing (ICIC 2024) is scheduled to take place from August 5th to 8th, 2024, in Tianjin, China. In the recently released acceptance notifications, our two latest research endeavors have been selected for oral presentation. They are respectively titled “RREH: Reconstruction Relations Embedded Hashing for Semi-Paired Cross-Modal Retrieval” and “Enhancing Emotion Prediction and Recognition in Conversation through Fine-Grained Emotional Cue Analysis and Cross-Modal Fusion”. We eagerly anticipate sharing the content of our research achievements with the Intelligent Computing community at ICIC2024.
[02/05/2024] $\bullet$ Groundbreaking Research on Emotion Transfer TTS Model Accepted at APWeb 2024. The Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM) is aiming at attracting professionals of different communities related to Web and Big Data who have common interests in interdisciplinary research to share and exchange ideas, experience and the underlying techniques and applications, including Web technologies, database systems, information management, software engineering and big data. In the latest acceptance notification, our latest paper titled with “RSET: Remapping-based Sorting Method for Emotion Transfer Speech Synthesis” on an advanced Text-to-Speech (TTS) model has been officially accepted by APWeb 2024. The innovative paper introduces a novel emotion transfer TTS model that surpasses traditional limitations experienced in emotion intensity controllable speech synthesis.
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.
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.
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.
Research aims to address various security threats and vulnerabilities associated with speech data, speech recognition systems, and voice communication.
Research topics related to music information retrieval, including song detection, singer identification, main melody extraction, and voice beautification.
The audio watermarking technique embeds messages into audio and accurately extracts messages from the watermarked audio. Traditional methods develop algorithms based on expert experience to embed watermarks into the time-domain or transform-domain of signals. With the development of deep neural networks, deep learning-based neural audio watermarking has emerged. Compared to traditional algorithms, neural audio watermarking achieves better robustness by considering various attacks during training. However, current neural watermarking methods suffer from low capacity and unsatisfactory imperceptibility. Additionally, the issue of watermark locating, which is extremely important and even more pronounced in neural audio watermarking, has not been adequately studied. In this paper, we design a dual-embedding watermarking model for efficient locating. We also consider the impact of the attack layer on the invertible neural network in robustness training, improving the model to enhance both its reasonableness and stability. Experiments show that the proposed model, IDEAW, can withstand various attacks with higher capacity and more efficient locating ability compared to existing methods. The code is available at https://largeaudiomodel.com/IDEAW.
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering{:} Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.
The purpose of emotion recognition in conversation (ERC) is to identify the emotion category of an utterance based on contextual information. Previous ERC methods relied on simple connections for cross-modal fusion and ignored the information differences between modalities, resulting in the model being unable to focus on modality-specific emotional information. At the same time, the shared information between modalities was not processed to generate emotions. Information redundancy problem. To overcome these limitations, we propose a cross-modal fusion emotion prediction network based on vector connections. The network mainly includes two stages{:} the multi-modal feature fusion stage based on connection vectors and the emotion classification stage based on fused features. Furthermore, we design a supervised inter-class contrastive learning module based on emotion labels. Experimental results confirm the effectiveness of the proposed method, demonstrating excellent performance on the IEMOCAP and MELD datasets.
The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) is set to be a major event for researchers, practitioners, and enthusiasts in the field of natural language processing (NLP). Taking place from November 12th to 16th in Miami, Florida, at the Hyatt Regency Miami Hotel, this conference promises to showcase cutting-edge research, innovative applications, and thought-provoking discussions.
中国自动化大会是由中国自动化学会创办的自动化、信息与智能科学领域顶级综合性学术会议,致力于为全球相关领域的专家学者和产业界的同仁提供展示创新成果、展望未来发展的高端学术平台,加强不同学科领域的交叉融合。瞄准世界科技前沿,引领科技发展方向。中国自动化大会历经十四载,走过杭州、西安、上海等地,汇聚智能科技的新理论、新技术、新成果,联通产学研用各界,为推动学科发展进步,促进产学研用深度融合,实现科技高水平自立自强做出了积极贡献。中国自动化学会定于2024年11月1-3日在青岛召开2024中国自动化大会,本次大会由中国自动化学会主办,青岛科技大学承办。
The Conference on Information and Knowledge Management (CIKM) provides an international forum for presentation and discussion of research on information and knowledge management, as well as recent advances on data and knowledge bases. The purpose of the conference is to identify challenging problems facing the development of future knowledge and information systems, and to shape future directions of research by soliciting and reviewing high quality, applied and theoretical research findings. An important part of the conference is the Workshops program which focuses on timely research challenges and initiatives. CIKM has a strong tradition of workshops devoted to emerging areas of database management, IR, and related fields.
The Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM) is aiming at attracting professionals of different communities related to Web and Big Data who have common interests in interdisciplinary research to share and exchange ideas, experience and the underlying techniques and applications, including Web technologies, database systems, information management, software engineering and big data. The 8th APWeb-WAIM joint international conference on Web and Big Data 2024 will be held in Jinhua, China, August30-Septemper 1, 2024.
The Association for Computational Linguistics (ACL) was established in 1962 and is the premier conference in the field of natural language processing (NLP) and computational linguistics. It is organized annually by the Association for Computational Linguistics. The ACL is one of the most influential and dynamic international academic organizations in the world. It holds an annual conference every summer, providing a platform for scholars to present papers and share the latest research findings. The association boasts members from over 60 countries and regions worldwide, representing the highest level of international computational linguistics in the NLP field.