CodecAttack perturbs audio in codec latent space with multi-bitrate EoT to achieve 85.5% average ASR on Opus-compressed Audio LLMs versus under 26% for waveform baselines, with transfer to MP3 and AAC.
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MusicRFM discovers interpretable concept directions in music model hidden states using RFM probes and injects them at inference to steer generation toward desired musical properties without retraining.
Introduces the first large-scale Persian music dataset and shows fine-tuned MusicGen produces compositions more aligned with Persian stylistic conventions via tag-based evaluation.
Step-Audio 2 integrates a latent audio encoder, reasoning-centric reinforcement learning, and discrete audio token generation into language modeling to deliver state-of-the-art performance on audio understanding and conversational benchmarks.
The work formalizes zero-shot symbolic drum editing as LLM reasoning over a drumroll grid notation, evaluates it on a new benchmark with automated symbolic unit tests, and reports up to 68% success across eight models.
Woosh is a new publicly released foundation model optimized for high-quality sound effect generation from text or video, showing competitive or better results than open alternatives like Stable Audio Open.
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
The paper introduces Musical Attention, an attention variant that incorporates eight musical features including metadata to generate more coherent and varied music than standard or strided attention baselines.
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