#audio-synthesis News & Analysis

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

8 articles

AIBearisharXiv – CS AI · May 277/10

🧠

Eroding Trust in Real Speech: A Large-Scale Study of Human Audio Deepfake Perception

A comprehensive listening study of 1,768 participants reveals that while humans remain similarly accurate at detecting fake audio (71.2%), they have significantly eroded trust in authentic speech, with real sample detection dropping from 72.7% to 64.1% compared to 2021 baselines. Modern commercial and language model-generated deepfakes pose the greatest challenge to human perception, though ML detectors maintain >94.5% accuracy across all conditions.

AINeutralarXiv – CS AI · Jun 86/10

🧠

Towards Unified Song Generation and Singing Voice Conversion with Accompaniment Co-Generation

Researchers introduce UniSinger, an AI framework that unifies song generation with singing voice conversion by enabling zero-shot speaker cloning and accompaniment co-generation. The system uses a multimodal diffusion transformer with curriculum learning to simultaneously handle vocal timbre control and musical accompaniment, advancing generative music production capabilities.

AINeutralarXiv – CS AI · Jun 26/10

🧠

HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark

Researchers introduce HAIM, a new dataset and benchmark for detecting AI integration across music production workflows, moving beyond binary AI-or-human classification to track granular stages of AI intervention including hybrid and mastered content. The work exposes critical limitations in current AI detection systems as generative music platforms like Suno and Udio achieve human-quality output.

AINeutralarXiv – CS AI · Jun 25/10

🧠

JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions

JenBridge is a new AI framework for generating long-form video soundtracks that maintain coherence across scene transitions using transformer-based generative models and LLM-directed transition selection. The system combines text-audio pretraining with video-domain adaptation and introduces the LVS Benchmark for evaluating soundtrack quality and transition naturalness.

AINeutralarXiv – CS AI · May 286/10

🧠

Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models

Researchers have developed techniques to enable fine-grained speaking style control in prompt-based text-to-speech models, allowing for smooth style transitions both between utterances and within single utterances. The approach uses embedding space interpolation for inter-utterance changes and attention mechanism modifications for intra-utterance style shifts, achieving high success rates in gender conversion and natural speaker transitions.

AINeutralarXiv – CS AI · May 286/10

🧠

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

Researchers introduce PlanAudio, an LLM-based framework that generates unified audio containing speech, sound, and composites directly from free-form text prompts. The approach uses a semantic latent chain-of-thought mechanism to bridge language understanding and acoustic synthesis, outperforming existing pipeline and baseline models across multiple audio scenarios.

AIBullisharXiv – CS AI · Mar 266/10

🧠

OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model

Researchers introduce OmniCustom, a new AI framework that simultaneously customizes both video identity and audio timbre in generated content. The system uses reference images and audio samples to create synchronized audio-video content while allowing users to specify spoken content through text prompts.

AINeutralarXiv – CS AI · Jun 234/10

🧠

Improving Text-to-Music Generation with Human Preference Rewards

Researchers submitted an entry to an academic text-to-music generation challenge using a learned human-preference reward system called TuneJury to improve model outputs. The approach combines five engineering optimizations on a 120M-parameter FluxAudio-S backbone, including reward conditioning, architectural sweeps, expert iteration, preference tuning, and inference post-processing.