A Demand-Driven Perspective on Generative Audio AI

Ben Sangbae Chon; Hyeongi Moon; Keunwoo Choi; Minsung Kang; Sangshin Oh

A Demand-Driven Perspective on Generative Audio AI

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arxiv 2307.04292 v1 pith:54J7O6OI submitted 2023-07-10 eess.AS cs.AI

Sangshin Oh , Minsung Kang , Hyeongi Moon , Keunwoo Choi , Ben Sangbae Chon This is my paper · ORCID

classification eess.AScs.AI

keywords audioresearchsurveyachieveachievinganalysisavailabilitybottleneck

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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.

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cs.SD 2024-12 conditional novelty 5.0 of 10

A caption-augmentation method that adds DSP-derived acoustic descriptors to text prompts gives a text-to-audio diffusion model controllable loudness, pitch, reverb, noise, brightness, fade, and duration.

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