With the advent of modern AI architectures, a shift has happened towards end-to-end architectures. This pivot has led to neural architectures being trained without domain-specific biases/knowledge, optimized according to the task. We in this paper, learn audio embeddings via diverse feature representations, in this case, domain-specific. For the case of audio classification over hundreds of categories of sound, we learn robust separate embeddings for diverse audio properties such as pitch, timbre, and neural representation, along with also learning it via an end-to-end architecture. We observe handcrafted embeddings, e.g., pitch and timbre-based, although on their own, are not able to beat a fully end-to-end representation, yet adding these together with end-to-end embedding helps us, significantly improve performance. This work would pave the way to bring some domain expertise with end-to-end models to learn robust, diverse representations, surpassing the performance of just training end-to-end models.
Nankai University（南开大学）；TMCC, College of Computer Science（TMCC计算机学院）；Hithink RoyalFlush AI Research Institute（Hithink RoyalFlush人工智能研究院）；University of Exeter（埃克塞特大学）
DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning
Shanghai Jiao Tong University（上海交通大学）；SJTU Paris Elite Institute of Technology, Shanghai Jiao Tong University（上海交通大学巴黎卓越工程师学院）；The Chinese University of Hong Kong（香港中文大学）
Gibberish is All You Need for Membership Inference Detection in Contrastive Language-Audio Pretraining
Beijing Electronic Science and Technology Institute（北京电子科技学院）；AVIC Nanjing Engineering Institute of Aircraft Systems（中航工业南京航空系统工程研究所）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
CLAP-Based Automatic Word Naming Recognition in Post-Stroke Aphasia
Signal Processing for Communication Group, Idiap Research Institute, Martigny, Switzerland（日内瓦瑞士IDIAP研究所信号处理与通信组）；Faculty of Psychology and Educational Science, University of Geneva, Geneva, Switzerland（日内瓦大学心理学与教育科学学院）
Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows
Centre for Vision, Speech and Signal Processing, University of Surrey（视觉、语音和信号处理中心，萨里大学）；Research and Development, British Broadcasting Corporation（英国广播公司研发部）
SmoothCLAP: Soft-Target Enhanced Contrastive Language\--Audio Pretraining for Affective Computing
CHI -- Chair of Health Informatics, TUM University Hospital, Munich, Germany（健康信息学系，塔尔博特大学医院，德国慕尼黑）；Huawei, Netherlands（华为，荷兰）；GLAM, Imperial College London, UK（GLAM，伦敦帝国学院，英国）
Language-Queried Target Sound Extraction Without Parallel Training Data
School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；North Carolina State University（北卡罗来纳州立大学）；ARC Lab, Tencent PCG（腾讯PCG ARC实验室）；Key Laboratory of System and Control, AMSS, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统与控制重点实验室）；The Chinese University of Hong Kong（香港中文大学）
Adapting Language-Audio Models as Few-Shot Audio Learners
Queen Mary University of London（伦敦玛丽女王大学）；Centre for Vision, Speech and Signal Processing（视觉、语音与信号处理中心）；Amazon Alexa（亚马逊Alexa）；The Alan Turing Institute（艾伦·图灵研究所）
COMET: Concept Space Dissection of the Modality Gap in Audio-Text Multimodal Contrastive Embeddings
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Centre for Vision, Speech, and Signal Processing (CVSSP), University of Surrey（Surrey 大学视觉、语音和信号处理中心）
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining
X-LANCE Lab, Shanghai Jiao Tong University（上海交通大学X-LANCE实验室）；SJTU Paris Elite Institute of Technology, Shanghai Jiao Tong University（上海交通大学巴黎卓越工程师学院）；Shanghai Innovation Institute（上海创新研究院）；The Chinese University of Hong Kong（香港中文大学）
