Verified claim · AI-ML · 82% confidence
WaveNet introduced in paper: WaveNet: A Generative Model for Raw Audio (van den Oord et al., 2016).
Last verified 2026-06-01 · Methodology veritas-v0.1 · 9b21377fcab5dd64
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Structured fields
- Subject
- WaveNet
- Predicate
introduced_in_paper- Object
- WaveNet: A Generative Model for Raw Audio (van den Oord et al., 2016)
- Confidence
- 82%
- Tags
- wavenet · audio-generation · dilated-convolutions · deepmind · van-den-oord · foundational · 2016
Sources (2)
[1] preprint · arXiv (Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu) · 2016-09-12
WaveNet: A Generative Model for Raw Audio“This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio.”
[2] docs · Hugging Face
WaveNet: A Generative Model for Raw Audio (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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WaveNet introduced in paper: WaveNet: A Generative Model for Raw Audio (van den Oord et al., 2016). — SourceScore Claim 9b21377fcab5dd64 (verified 2026-06-01). https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.jsonEmbed this claim
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The record lists 2 cited source(s): arXiv (Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu), Hugging Face. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
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cURL
curl https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "WaveNet introduced in paper: WaveNet: A Generative Model for Raw Audio (van den Oord et al., 2016)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "WaveNet introduced in paper: WaveNet: A Generative Model for Raw Audio (van den Oord et al., 2016)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_wavenet_fact() -> dict:
"""Fetch the verified SourceScore claim for WaveNet."""
r = httpx.get("https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.json")
return r.json()