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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Frequently asked questions
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Yes — SourceScore verified this claim with 82% confidence as of 2026-06-01. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
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Evidence comes from 2 primary sources: arXiv (Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/9b21377fcab5dd64.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-06-01 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
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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");
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// "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()