#generative-audio News & Analysis

5 articles tagged with #generative-audio. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AINeutralarXiv – CS AI · Jun 16/10

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ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment

Researchers introduce ImmersiveTTS, an AI model that generates natural speech integrated within environmental audio contexts using multimodal diffusion transformers and domain-specific representation alignment. The advancement addresses a key challenge in audio generation: seamlessly combining speech with background environmental sounds while maintaining acoustic quality and intelligibility.

AINeutralarXiv – CS AI · May 126/10

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Remix the Timbre: Diffusion-Based Style Transfer Across Polyphonic Stems

Researchers introduce MixtureTT, a diffusion-based system for timbre transfer in polyphonic music that directly processes mixed audio rather than separating instruments first. The approach outperforms existing separate-then-transfer pipelines by modeling dependencies across multiple stems simultaneously, reducing inference costs and eliminating source separation artifacts.

AIBullisharXiv – CS AI · May 76/10

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JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions

Researchers introduce JASTIN, an instruction-driven framework that combines frozen audio encoders with fine-tuned LLMs to evaluate generative audio models with zero-shot capabilities. The approach achieves state-of-the-art correlation with human ratings across speech, sound, and music evaluation tasks without task-specific retraining.

AINeutralarXiv – CS AI · Mar 126/10

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Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning

Researchers propose HIR-SDD, a new framework combining Large Audio Language Models with human-inspired reasoning to detect speech deepfakes. The method aims to improve generalization across different audio domains and provide interpretable explanations for deepfake detection decisions.

AINeutralarXiv – CS AI · Mar 175/10

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Evaluating Semantic Fragility in Text-to-Audio Generation Systems Under Controlled Prompt Perturbations

Researchers evaluated the semantic fragility of text-to-audio generation systems, finding that small changes in prompts can lead to substantial variations in generated audio output. While larger models like MusicGen-large showed better semantic consistency, all models exhibited persistent divergence in acoustic and temporal characteristics even when semantic similarity remained high.