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Gaëtan Hadjeres
Sony AI
M
Marc Ferras
Sony AI
K
Khaled Koutini
Sony AI
B
Benno Weck
Sony AI
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References
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Founder's Pitch
"Harness Sony AI's 'Woosh' for groundbreaking, high-quality sound effects generation for multimedia solutions."
Commercial Viability Breakdown
0-10 scaleHigh Potential
4/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
This research addresses the demand in media production for high-quality, quick-turnaround audio solutions specifically sound effects which are critical for enhancing immersive experiences in games, films, and virtual reality.
Product Angle
Productize 'Woosh' as a SaaS platform providing customizable sound effects generation for creative industries such as gaming, film, and VR/AR development.
Disruption
Woosh can replace traditional sound engineering tasks by automating sound effects generation, streamlining the production process, and enabling more personalized and scalable audio in multimedia content.
Product Opportunity
The multimedia industry, including gaming, film production, and VR/AR applications, is rapidly growing and demands high-quality and customizable sound effects which this tool can deliver efficiently, offering significant time and cost savings.
Use Case Idea
Developing an API service that integrates Woosh's capabilities to allow video game developers, filmmakers, and VR environments to generate custom, high-quality sound effects on demand based on textual descriptions or video context.
Science
The paper presents a series of models under the 'Woosh' project by Sony AI, which includes a sound effects foundation model. It features an audio encoder-decoder, text-conditioned audio generation, and a distilled model for quick inference, leveraging latent diffusion models and the VOCOS architecture for high-quality generative sound effects.
Method & Eval
The models were tested against existing systems like StableAudio-Open and TangoFlux, demonstrating superior performance in generating high-quality audio effects, with a variety of metrics such as Log-mel Distance and SI-SDR showing substantial improvements.
Caveats
The system's efficiency is subject to the diversity and quality of initial datasets, and misalignment in text-audio can lead to suboptimal results, also challenges in creative industries' acceptance of automated sound generation could pose risks.
Author Intelligence
Gaëtan Hadjeres
Sony AI
Marc Ferras
Sony AI
Khaled Koutini
Sony AI
Benno Weck
Sony AI
Alexandre Bittar
Sony AI
Thomas Hummel
Sony AI
Zineb Lahrici
Sony AI
Hakim Missoum
Sony AI
Joan Serrà
Sony AI
Yuki Mitsufuji
Sony AI and Sony Group Corporation