SEA-LM: Egocentric Spatial Audio Understanding for Wearable Microphone Arrays
cs.SD, cs.AI
Submitted: 2026-10-04
Updated: 2026-10-07
Code: https://github.com/partha2409/DCASE2024_seld_baseline
Sources
- BEATs: Audio Pre-Training with Acoustic Tokenizers
- Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
- Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities
- AST: Audio Spectrogram Transformer
- SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models
- The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models
- LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
- BAT: Learning to Reason about Spatial Sounds with Large Language Models
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