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 | submitted by /u/towngrizzlytown to r/longevity [link] [comments] |
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1-dan master of the unyielding fist of Bayesian inference
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Berkeley, CA
Berkeley, CA
Berkeley, CA
I only post this because LeCun is one of the most enthusiastic researchers about coming up with new AI architectures to build human-level AI. Not sure this is the best timing for fundraising with all the bubble talk getting louder, but oh well.
Excited to see what comes out of this!
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Berkeley, CA
![[R] WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms](https://b.thumbs.redditmedia.com/wh2Qpbb4RvPSt7jP0XrBkhO4KvgcJNxEY5kbJokh0cs.jpg "[R] WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms") | Hey All, We have just released our new pre-print on WavJEPA. WavJEPA is an audio foundation model that operates on raw waveforms (time-domain). Our results showcase that WavJEPA excel at general audio representation tasks with a fraction of compute and training data. In short, WavJEPA leverages JEPA like semantic token prediction tasks in the latent space. This make WavJEPA stand out from other models such as Wav2Vec2.0, HuBERT, and WavLM that utilize speech level token prediction tasks. In our results, we saw that WavJEPA was extremely data efficent. It exceeded the downstream performances of other models with magnitudes of less compute required. We were further very interested in models with good robustness to noise and reverberations. Therefore, we benchmarked state-of-the-art time domain audio models using Nat-HEAR (Naturalistic HEAR Benchmark with added reverb + noise). The differences between HEAR and Nat-HEAR indicated that WavJEPA was very robust compared to the other models. Possibly thanks to semantically rich tokens. Furthermore, in this paper we proposed WavJEPA-Nat. WavJEPA-Nat is trained with naturalistic scenes (reverb + noise + spatial), and is optimized for learning robust representations. We showed that WavJEPA-Nat is more robust than WavJEPA on naturalistic scenes, and performs better on dry scenes. As we are an academic institution, we did not have huge amounts of compute available. We tried to make the best out of it, and with clever tricks we managed to create a training methadology that is extremely fast and efficent. To go more in-depth please refer to our paper and the code: Paper: https://arxiv.org/abs/2509.23238 And, to use WavJEPA models, please use our huggingface endpoint. https://huggingface.co/labhamlet/wavjepa-base Looking forward to your thoughts on the paper! [link] [comments] |
Berkeley, CA
Berkeley, CA
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