mlsanityData to 7 Oct 2026

A Demand-Driven Perspective on Generative Audio AI

arXiv 2307.04292 ↗First posted 10 Jul 2023eess.AS cs.AIcited by 4

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Abstract (arXiv)

To achieve successful deployment of AI research, it is crucial to understand the demands of the industry. In this paper, we present the results of a survey conducted with professional audio engineers, in order to determine research priorities and define various research tasks. We also summarize the current challenges in audio quality and controllability based on the survey. Our analysis emphasizes that the availability of datasets is currently the main bottleneck for achieving high-quality audio generation. Finally, we suggest potential solutions for some revealed issues with empirical evidence.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 10 Jul 2023.

Builds on

22 of 30 references.

References · 22

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10 more
  1. 2023Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Modelscited by 323
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  5. 2021FoleyGAN: Visually Guided Generative Adversarial Network-Based Synchronous Sound Generation in Silent Videoscited by 20
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2024Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Soundcited by 16
  2. 2023EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesiscited by 15
  3. 2025Hear What Matters! Text-conditioned Selective Video-to-Audio Generationcited by 3
  4. 2026An investigation of AI integration in sound designer workflows and experiencescited by 0