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Zhejiang University
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A unified taxonomy of audio editing tasks reveals the transformative potential of foundation models in reshaping how we interact with sound.
Full-duplex dialogue systems are often mischaracterized, with many claiming capabilities they cannot deliver due to training limitations.
Spatial-Omni achieves superior spatial audio understanding by seamlessly integrating FOA encoding into existing LLMs, outperforming traditional models without compromising general audio processing.
SwanVoice leaps ahead in zero-shot TTS by nailing expressive, multi-speaker dialogue with a single model, finally bridging the gap between monologue quality and conversational coherence.
SwanSphere achieves real-time, high-fidelity spatial audio generation from panoramic video and text, overcoming the latency and spatial accuracy limitations of existing methods.
