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A Multi-Agent AI Framework for Immersive Audiobook Production through Spatial Audio and Neural Narration

Shaja Arul Selvamani, Nia D'Souza Ganapathy

TL;DR

This paper presents a multi-agent AI framework for immersive audiobook production that combines neural text-to-speech (FastSpeech 2, VALL-E) with diffusion-based spatial audio and neural acoustic fields to generate synchronized, scene-aware narration. Temporal alignment and richness of the soundscape are achieved through DTW, spectrotemporal integration, RNNs, and multiscale modulation, while modular agents handle TTS, spatial sound design, and mixing, enabling scalable, cross-environment outputs. The work emphasizes collaborative agent interactions and human-in-the-loop refinement, addressing ethical considerations and aiming for accessible, multilingual narration with personalized voices. Practically, the framework promises faster production, richer educational and storytelling experiences, and broader accessibility, with future work focusing on hardware acceleration, personalized listening experiences, and cross-modal integration with AR/VR platforms.

Abstract

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.

A Multi-Agent AI Framework for Immersive Audiobook Production through Spatial Audio and Neural Narration

TL;DR

This paper presents a multi-agent AI framework for immersive audiobook production that combines neural text-to-speech (FastSpeech 2, VALL-E) with diffusion-based spatial audio and neural acoustic fields to generate synchronized, scene-aware narration. Temporal alignment and richness of the soundscape are achieved through DTW, spectrotemporal integration, RNNs, and multiscale modulation, while modular agents handle TTS, spatial sound design, and mixing, enabling scalable, cross-environment outputs. The work emphasizes collaborative agent interactions and human-in-the-loop refinement, addressing ethical considerations and aiming for accessible, multilingual narration with personalized voices. Practically, the framework promises faster production, richer educational and storytelling experiences, and broader accessibility, with future work focusing on hardware acceleration, personalized listening experiences, and cross-modal integration with AR/VR platforms.

Abstract

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.
Paper Structure (29 sections, 10 equations, 1 figure)

This paper contains 29 sections, 10 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Neural Text-to-Speech Synthesis
  4. Spatial Audio Generation Techniques
  5. Multi-Agent Architectures for Audio Production
  6. Ethical and Market Considerations
  7. Study of Existing Approaches in Audiobook Generation, Spatial Audio Synthesis, and Multi-Agent Systems
  8. Audiobook Generation Approaches
  9. Spatial Audio Synthesis Techniques
  10. Multi-Agent Systems for Audiobook Production
  11. Human-in-the-Loop in Audiobook
  12. Methodology
  13. Text-to-Speech Conversion
  14. Spatial Sound Effect Generation
  15. Temporal Audio Integration Techniques
  16. ...and 14 more sections

Figures (1)

  • Figure 1: Overview of the proposed multi-agent framework for audiobook generation.