Emerging Trends in Generative-AI Based Audio Processing

Dear Colleagues,

Generative AI continues to transform audio processing through rapid advances in deep learning, signal modeling, and interdisciplinary applications. Beyond traditional domains like speech synthesis and music generation, research has expanded to include deepfake detection, audio watermarking, audio for embodied AI, and multimodal systems that integrate sound with text, vision, and haptic feedback. Meanwhile, emerging large-scale foundation models and novel architectures such as diffusion-based generators and transformer-based pipelines have presented new opportunities to synthesize and analyze audio in previously unattainable ways.

This Special Issue aims to provide a comprehensive and forward-looking collection of innovative methodologies, theoretical advances, and real-world applications in generative AI for audio. We invite contributions spanning foundational research, rigorous evaluations, proof-of-concept prototypes, and production-level deployments to foster collaborative and multidisciplinary exchange among industry practitioners, academic researchers, and policymakers.

(1) Introduction

Generative AI has rapidly evolved from a niche research topic to a major driver of innovation across speech technologies, music production, sound design, and beyond. Traditional deep learning frameworks like GANs and VAEs have paved the way for more sophisticated models, such as diffusion models and large transformer-based architectures that comprise audio foundation models, enabling unprecedented fidelity in synthesized audio. These generative systems can summarize, translate, and contextualize audio content.

Such advances introduce new opportunities and challenges. Applications like audio watermarking and deepfake detection address urgent concerns surrounding authenticity and ethical use of generative content. Meanwhile, embodied AI leverages audio for a human–robot interaction, facilitating more naturalistic communication in assistive devices, virtual environments, and physical robotics. As generative models become more adept at zero-shot or few-shot learning, they open the door to cross-lingual and cross-modal scenarios, transforming how audio is created, consumed, and regulated.

The entertainment, accessibility, healthcare, education, and robotics industries increasingly rely on robust and intelligent audio systems. This growing dependence underscores the need for computational efficiency, scalability, and responsible AI guidelines. By uniting machine learning, signal processing, music information retrieval, cognitive science, and law/ethics, this Special Issue aims to illuminate the state of the art and the future directions of generative-AI-based audio research.

(2) Aim of the Special Issue

1. Document Emerging Frontiers: Capture the latest developments in foundation models, multimodal architectures, and diffusion-based methods, highlighting their capabilities and limitations in audio.
2. Promote Responsible Innovation: Facilitate research on deepfake detection, audio watermarking, and content authentication to ensure ethical practices and trust in generative audio.
3. Foster Interdisciplinary Collaboration: Bring together experts from the signal processing, robotics, music technology, law, and healthcare industries to tackle real-world challenges, from embodied AI to therapeutic applications.
4. Encourage Novel Evaluation Protocols: Showcase standardized metrics, benchmarking datasets, and best practices for rigorously evaluating generative audio models, including subjective and objective measures.
5. Identify Future Directions: Discuss emerging research questions around scalability, real-time deployment, privacy, and regulatory frameworks to shape upcoming advancements in generative AI-based audio.

By providing a unified platform, this Special Issue aims to serve as a reference point for academic inquiry and industrial deployment, charting a course for responsible and impactful progress in generative-AI-based audio processing.

(3) Suggested Themes and Article Types for Submissions

We welcome original research articles, review papers, perspective pieces, and system demonstrations on the following topics (among others):

1. Advanced Generative Architectures for Audio
• Diffusion models, transformers, and hybrid approaches (GANs, VAEs, and diffusion).
• Foundation models for audio that enable zero-shot or few-shot generation.
2. Speech Synthesis and Voice Modeling
• High-fidelity TTS (text-to-speech) with prosody control, emotional expression, and multilingual support.
• Voice cloning and style transfer techniques with attention to authenticity and ethics.
3. Music Generation and Composition
• Generative music models with real-time collaboration, interactive composition tools, and cross-genre adaptation.
• Creative AI frameworks for remixing, personalization, and user-driven musical experiences.
4. Audio Enhancement and Restoration
• Noise reduction, dereverberation, super-resolution, and audio inpainting.
• Generative methods for historical/archival audio restoration and content preservation.
5. Audio Watermarking and Authentication
• Novel watermarking strategies that are robust against generative manipulation.
• Protection of intellectual property and forensic analysis of AI-synthesized audio.
6. Deepfake Detection and Content Verification
• Algorithms for detecting synthesized or manipulated speech and music.
• Robust authentication systems and frameworks for identifying generative artifacts.
7. Audio Summarization and Reasoning
• Techniques for condensing long-form audio, such as meetings, lectures, or podcasts.
• Exploiting natural language processing (NLP) for cross-modal summarization (audio-to-text).
• Integrating reasoning capabilities to enable context-aware audio interactions.
8. Embodied AI and Human–Robot Interaction
• Speech-driven robot control, sound-based localization, and audio-based sensor fusion in robotics.
• Generative audio for virtual reality (VR), augmented reality (AR), and immersive simulations.
9. Multimodal and Cross-Modal Generative Models
• Text-to-audio, image-to-sound, or sensor-to-audio pipelines for creative content generation.
• Combining linguistic, visual, and auditory cues for contextual and situational understanding.
10. Ethical, Societal, and Regulatory Concerns
• Responsible AI guidelines, ethical design, and privacy-preserving approaches.
• Legal frameworks for intellectual property, user consent, and content licensing in generative audio.
11. Computational Efficiency, Deployment, and Scaling
• Resource-constrained model deployment (edge computing and mobile devices).
• Distributed training, federated learning, and cloud-based solutions for large-scale audio.
12. Evaluation Metrics, Benchmarks, and Standardization
• Objective and subjective evaluation methodologies for generative audio (e.g., MOS and preference tests).
• Open-source datasets, community challenges, and reproducibility initiatives.
13. Applications in Accessibility, Healthcare, and Education
• Assistive technologies for users who are blind or hard of hearing (text-to-speech and speech-to-text).
• Therapeutic and diagnostic applications employing generative audio (e.g., speech therapy).
• Interactive learning environments leveraging real-time generative feedback.

We encourage researchers, practitioners, and industry experts to contribute innovative, rigorous, and impactful work. By bringing together a broad spectrum of approaches and perspectives, this Special Issue will push the boundaries of current generative-AI-based audio research and lay the groundwork for future advancements. For more information about submission guidelines and timelines, please refer to the journal’s website or contact the guest editors directly.

We look forward to your innovative contributions and fostering vibrant discussions on the future of generative AI in audio processing.

Dr. Rita Singh
Guest Editor

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• generative AI
• audio watermarking
• deepfake detection
• speech processing
• music generation
• audio foundation models
• neural audio reasoning
• embodied AI
• audio summarization
• multimodal integration