title: Audio Dialogues: Dialogues dataset for audio and music understanding
summary: Existing datasets for audio understanding primarily focus on single-turn
  interactions (i.e. audio captioning, audio question answering) for describing
  audio in natural language, thus limiting understanding audio via interactive
  dialogue. To address this gap, we introduce Audio Dialogues: a multi-turn
  dialogue dataset containing 163.8k samples for general audio sounds and music.
  In addition to dialogues, Audio Dialogues also has question-answer pairs to
  understand and compare multiple input audios together. Audio Dialogues
  leverages a prompting-based approach and caption annotations from existing
  datasets to generate multi-turn dialogues using a Large Language Model (LLM).
  We evaluate existing audio-augmented large language models on our proposed
  dataset to demonstrate the complexity and applicability of Audio Dialogues. Our
  code for generating the dataset will be made publicly available. Detailed
  prompts and generated dialogues can be found on the demo website
  https://audiodialogues.github.io/.
paper_authors._id: 66188dbbc69a25d5e72040b5
paper_authors.name: Arushi Goel
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-04-12T01:26:19.902Z
paper_authors._id: 65c0b5babf441a1953586df7
paper_authors.avatarUrl: /avatars/b767ab08dc096e0aeac29acfcb26d39e.svg
paper_authors.fullname: Arushi Goel
paper_authors.type: user
paper_authors.user: goarushi27
paper_authors._id: 66188dbbc69a25d5e72040b6
paper_authors.name: Zhifeng Kong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:27:34.590Z
paper_authors._id: 652a4dfc36f031c5e6f8b8a6
paper_authors.avatarUrl: /avatars/9fb56b025dc25f91ca6c31136eaf74b2.svg
paper_authors.fullname: Zhifeng Kong
paper_authors.type: user
paper_authors.user: ZhifengKong
paper_authors._id: 66188dbbc69a25d5e72040b7
paper_authors.name: Rafael Valle
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:27:52.372Z
paper_authors._id: 6440ddd65d600fb09518daa8
paper_authors.avatarUrl: /avatars/ac5898afd2082d230e2ebf6fb867ad4f.svg
paper_authors.fullname: Rafael Valle
paper_authors.type: user
paper_authors.user: rafaelvalle
paper_authors._id: 66188dbbc69a25d5e72040b8
paper_authors.name: Bryan Catanzaro
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:27:58.405Z
paper_authors._id: 6311021788942700629e6247
paper_authors.avatarUrl: /avatars/e7adc1632b76e80e7e4a590033d1c20a.svg
paper_authors.fullname: Bryan Catanzaro
paper_authors.type: user
paper_authors.user: ctnzr
paper_title: Audio Dialogues: Dialogues dataset for audio and music understanding
paper_summary: Existing datasets for audio understanding primarily focus on single-turn
  interactions (i.e. audio captioning, audio question answering) for describing
  audio in natural language, thus limiting understanding audio via interactive
  dialogue. To address this gap, we introduce Audio Dialogues: a multi-turn
  dialogue dataset containing 163.8k samples for general audio sounds and music.
  In addition to dialogues, Audio Dialogues also has question-answer pairs to
  understand and compare multiple input audios together. Audio Dialogues
  leverages a prompting-based approach and caption annotations from existing
  datasets to generate multi-turn dialogues using a Large Language Model (LLM).
  We evaluate existing audio-augmented large language models on our proposed
  dataset to demonstrate the complexity and applicability of Audio Dialogues. Our
  code for generating the dataset will be made publicly available. Detailed
  prompts and generated dialogues can be found on the demo website
  https://audiodialogues.github.io/.
paper_ai_summary: A new multi-turn dialogue dataset for audio, Audio Dialogues, is introduced to enhance understanding and dialogue capabilities in audio processing using a Large Language Model.
paper_ai_keywords: audio captioning audio question answering multi-turn dialogue Large Language Model audio-augmented large language models