Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models.
Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context of generative audio models (in the end, the ultimate goal is to build the best audio generative models)
Explore the trade-offs of continuous (as typically used for diffusion models) vs. discrete audio representations (as typically used for autoregressive models).
Develop benchmarking pipelines for codec evaluation
Conduct initial experiments with generative models to verify that a new candidate codec is actually useful for our downstream tasks.
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Ideal Candidate Profile
Strong background in deep learning for audio: neural codecs, source separation, speech models, or generative audio systems
Specific hands-on experience in designing and training neural audio codecs
Solid understanding of audio signal processing fundamentals
Strong track record (research and/or open-source) in the field of audio ML
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Nice to Have
Hands-on experience with generative audio models and good intuition of how the choice of the codec influences the training and performance of the generative model
* Strong publication record (e.g., NeurIPS, ICML, ICLR, Interspeech, ICASSP, WASPAA)
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