PhaseCoder: Geometry-Agnostic Spatial Audio for Multimodal LLMs
A recent study presents PhaseCoder, a transformer-based spatial audio encoder that operates independently of microphone configurations, allowing multimodal large language models (LLMs) to interpret spatial audio from various microphone arrays. The research, titled 'PhaseCoder: Microphone Geometry-Agnostic Spatial Audio Understanding for Multimodal LLMs,' was uploaded to arXiv (ID 2601.21124) in the Computer Science > Sound section. Traditional multimodal LLMs treat audio as a mono stream, overlooking crucial spatial details vital for embodied AI, while current spatial audio models are limited to specific microphone setups. PhaseCoder utilizes raw multichannel audio and microphone positions to achieve localization and generate effective spatial embeddings. The team shows that the Gemma 3n LLM can be adapted to process 'Spatial Audio Tokens' from PhaseCoder. This encoder sets new benchmarks for microphone-invariant localization and allows LLMs to execute intricate spatial reasoning and precise transcription tasks from any microphone array. The paper also includes submission history, references, citations, and links to relevant code and data, making it significant for AI, spatial audio, and multimodal learning, with implications for robotics, augmented reality, and assistive technologies.
Key facts
- PhaseCoder is a transformer-only spatial audio encoder.
- It is agnostic to microphone geometry.
- It takes raw multichannel audio and microphone coordinates as inputs.
- It performs localization and produces spatial embeddings.
- Gemma 3n LLM can be fine-tuned to reason over 'Spatial Audio Tokens'.
- Achieves state-of-the-art results on microphone-invariant localization benchmarks.
- Enables LLM to perform complex spatial reasoning and targeted transcription from arbitrary microphone arrays.
- Paper submitted to arXiv with ID 2601.21124.
Entities
Institutions
- arXiv