Despite its widespread adoption as the prominent neural architecture, the Transformer has spurred several independent lines of work to address its limitations. One such approach is selective state space models, which have demonstrated promising results for language modelling. However, their feasibility for learning self-supervised, general-purpose audio representations is yet to be investigated. This work proposes Audio Mamba, a selective state space model for learning general-purpose audio representations from randomly masked spectrogram patches through self-supervision. Empirical results on ten diverse audio recognition downstream tasks show that the proposed models, pretrained on the AudioSet dataset, consistently outperform comparable self-supervised audio spectrogram transformer (SSAST) baselines by a considerable margin and demonstrate better performance in dataset size, sequence length and model size comparisons.
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University Health Network（大学网络健康中心）；Peter Munk Cardiac Centre（彼得·芒克心脏中心）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学检验医学与病理生物学系）；AI Hub, University Health Network（大学网络健康中心人工智能中心）
HELIX: Scaling Raw Audio Understanding with Hybrid Mamba-Attention Beyond the Quadratic Limit
Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany（弗赖堡大学计算机科学系，德国弗赖堡市）；Department of Computer Engineering（计算机工程系）；Information Technology, Veermata Jijabai Technological Institute, Mumbai, India（维尔玛塔·吉贾拜技术学院信息科技系，印度孟买）
TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms
An overview of neural architectures for self-supervised audio representation learning from masked spectrograms
Department of Electronic Systems, Aalborg University（电子系统系，奥尔堡大学）；Pioneer Centre for Artificial Intelligence（人工智能先锋中心）；National and Kapodistrian University of Athens（雅典国家与卡波蒂斯坦大学）
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology（电气工程、数学和计算机科学学院，代尔夫特理工大学）；Leiden Institute of Advanced Computer Science (LIACS), Leiden University（先进计算机科学研究所（LIACS），莱顿大学）
RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）
Cross-attention Inspired Selective State Space Models for Target Sound Extraction
National Key Laboratory of General Artificial Intelligence（国家级通用人工智能重点实验室）；School of Intelligence Science and Technology（智能科学与技术学院）；Peking University（北京大学）
Protecting Bystander Privacy via Selective Hearing in Audio LLMs
VRAIN, Universitat Politècnica de València（VRAIN，瓦伦西亚理工大学）；Department of Engineering, University of Cambridge（剑桥大学工程系）；INGENIO (CSIC-Universitat Politècnica de València)（INGENIO（CSIC-瓦伦西亚理工大学））
ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China（海洋科学与技术学院，西北工业大学，西安，中国）；School of Automation, Northwestern Polytechnical University, Xi’an, China（自动化学院，西北工业大学，西安，中国）；School of Electrical Engineering, KAIST, Daejeon, Republic of Korea（电气工程学院，韩国成均馆大学，大田，韩国）
Selective Listening: Mechanism-Guided Control of Audio Influence in Large Audio-Language Models
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）
