A Multi-Agent AI Framework for Immersive Audiobook Production through Spatial Audio and Neural Narration
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915277599408128 |
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| author | Selvamani, Shaja Arul Ganapathy, Nia D'Souza |
| author_facet | Selvamani, Shaja Arul Ganapathy, Nia D'Souza |
| contents | This research introduces an innovative AI-driven multi-agent framework specifically designed for creating immersive audiobooks. Leveraging neural text-to-speech synthesis with FastSpeech 2 and VALL-E for expressive narration and character-specific voices, the framework employs advanced language models to automatically interpret textual narratives and generate realistic spatial audio effects. These sound effects are dynamically synchronized with the storyline through sophisticated temporal integration methods, including Dynamic Time Warping (DTW) and recurrent neural networks (RNNs). Diffusion-based generative models combined with higher-order ambisonics (HOA) and scattering delay networks (SDN) enable highly realistic 3D soundscapes, substantially enhancing listener immersion and narrative realism. This technology significantly advances audiobook applications, providing richer experiences for educational content, storytelling platforms, and accessibility solutions for visually impaired audiences. Future work will address personalization, ethical management of synthesized voices, and integration with multi-sensory platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent AI Framework for Immersive Audiobook Production through Spatial Audio and Neural Narration Selvamani, Shaja Arul Ganapathy, Nia D'Souza Sound Human-Computer Interaction Multiagent Systems Multimedia Audio and Speech Processing This research introduces an innovative AI-driven multi-agent framework specifically designed for creating immersive audiobooks. Leveraging neural text-to-speech synthesis with FastSpeech 2 and VALL-E for expressive narration and character-specific voices, the framework employs advanced language models to automatically interpret textual narratives and generate realistic spatial audio effects. These sound effects are dynamically synchronized with the storyline through sophisticated temporal integration methods, including Dynamic Time Warping (DTW) and recurrent neural networks (RNNs). Diffusion-based generative models combined with higher-order ambisonics (HOA) and scattering delay networks (SDN) enable highly realistic 3D soundscapes, substantially enhancing listener immersion and narrative realism. This technology significantly advances audiobook applications, providing richer experiences for educational content, storytelling platforms, and accessibility solutions for visually impaired audiences. Future work will address personalization, ethical management of synthesized voices, and integration with multi-sensory platforms. |
| title | A Multi-Agent AI Framework for Immersive Audiobook Production through Spatial Audio and Neural Narration |
| topic | Sound Human-Computer Interaction Multiagent Systems Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.04885 |