title: Audio Mamba: Bidirectional State Space Model for Audio Representation
    Learning
summary: Transformers have rapidly become the preferred choice for audio
  classification, surpassing methods based on CNNs. However, Audio Spectrogram
  Transformers (ASTs) exhibit quadratic scaling due to self-attention. The
  removal of this quadratic self-attention cost presents an appealing direction.
  Recently, state space models (SSMs), such as Mamba, have demonstrated potential
  in language and vision tasks in this regard. In this study, we explore whether
  reliance on self-attention is necessary for audio classification tasks. By
  introducing Audio Mamba (AuM), the first self-attention-free, purely SSM-based
  model for audio classification, we aim to address this question. We evaluate
  AuM on various audio datasets - comprising six different benchmarks - where it
  achieves comparable or better performance compared to well-established AST
  model.
paper_authors._id: 66612ef00dbf4ebf86c08924
paper_authors.name: Mehmet Hamza Erol
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-06T08:47:35.852Z
paper_authors._id: 6404a5c40ab5e22719f3e105
paper_authors.avatarUrl: /avatars/f683bd957502162bad10039c28c8c244.svg
paper_authors.fullname: Mehmet Hamza Erol
paper_authors.type: user
paper_authors.user: mhamzaerol
paper_authors._id: 66612ef00dbf4ebf86c08925
paper_authors.name: Arda Senocak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-06T08:47:43.691Z
paper_authors._id: 658a6098d1331d552b293e58
paper_authors.avatarUrl: /avatars/bb06fc9a47cb78ca9b4e9e7da5cc6b58.svg
paper_authors.fullname: Arda Senocak
paper_authors.type: user
paper_authors.user: ardasnck
paper_authors._id: 66612ef00dbf4ebf86c08926
paper_authors.name: Jiu Feng
paper_authors._id: 66612ef00dbf4ebf86c08927
paper_authors.name: Joon Son Chung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-06T08:48:07.919Z
paper_authors._id: 647dcc87e4d52fe0e01c8ffc
paper_authors.avatarUrl: /avatars/56fbf5604dc4082b0c494bd49f345c71.svg
paper_authors.fullname: Joon Son Chung
paper_authors.type: user
paper_authors.user: joonson
paper_title: Audio Mamba: Bidirectional State Space Model for Audio Representation
    Learning
paper_summary: Transformers have rapidly become the preferred choice for audio
  classification, surpassing methods based on CNNs. However, Audio Spectrogram
  Transformers (ASTs) exhibit quadratic scaling due to self-attention. The
  removal of this quadratic self-attention cost presents an appealing direction.
  Recently, state space models (SSMs), such as Mamba, have demonstrated potential
  in language and vision tasks in this regard. In this study, we explore whether
  reliance on self-attention is necessary for audio classification tasks. By
  introducing Audio Mamba (AuM), the first self-attention-free, purely SSM-based
  model for audio classification, we aim to address this question. We evaluate
  AuM on various audio datasets - comprising six different benchmarks - where it
  achieves comparable or better performance compared to well-established AST
  model.
paper_ai_keywords: transformers CNNs Audio Spectrogram Transformers (ASTs) self-attention quadratic scaling state space models (SSMs) Mamba Audio Mamba (AuM) audio classification tasks