SwiftAudio performs caption-only distillation of a one-step TTA diffusion model by adapting VSD to audio with temporal smoothness regularization, achieving SOTA among one-step methods on AudioCaps and Clotho using ~45K captions.
Audioldm 2: Learning holistic audio generation with self-supervised pretraining
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Break-the-Beat! renders drum MIDI audio that matches the timbre of a reference clip by fine-tuning a text-to-audio model with a content encoder and hybrid conditioning on a new paired dataset.
A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
A coarse-to-fine hybrid MMDiT/DiT audio editor trained with rectified flow matching improves fidelity and cuts edit time versus prior instruction-guided baselines on synthetic overlapping-event tasks.
A survey that organizes audio SSL into five objective paradigms, relates their demands to architectural biases, and interprets downstream applications as tests of generalization.
citing papers explorer
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SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation
SwiftAudio performs caption-only distillation of a one-step TTA diffusion model by adapting VSD to audio with temporal smoothness regularization, achieving SOTA among one-step methods on AudioCaps and Clotho using ~45K captions.
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Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis
Break-the-Beat! renders drum MIDI audio that matches the timbre of a reference clip by fine-tuning a text-to-audio model with a content encoder and hybrid conditioning on a new paired dataset.
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Language-Guided Multimodal Texture Authoring via Generative Models
A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
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RFM-Editing 2: Text-Guided Audio Editing with Rectified Flow Matching and Coarse-to-Fine Diffusion Transformers
A coarse-to-fine hybrid MMDiT/DiT audio editor trained with rectified flow matching improves fidelity and cuts edit time versus prior instruction-guided baselines on synthetic overlapping-event tasks.
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From Objectives to Applications: Aligning Architectural Biases in Audio Self-Supervised Learning
A survey that organizes audio SSL into five objective paradigms, relates their demands to architectural biases, and interprets downstream applications as tests of generalization.