Pith Number

pith:FTD7BVHP

pith:2016:FTD7BVHPURSHPJGOPLUEFR5W7H

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WaveNet: A Generative Model for Raw Audio

Aaron van den Oord, Alex Graves, Andrew Senior, Heiga Zen, Karen Simonyan, Koray Kavukcuoglu, Nal Kalchbrenner, Oriol Vinyals, Sander Dieleman

WaveNet generates raw audio waveforms by predicting each sample from all previous ones and yields more natural text-to-speech than prior systems.

arxiv:1609.03499 v2 · 2016-09-12 · cs.SD · cs.LG

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin.

C2weakest assumption

The assumption that human listener ratings of naturalness provide a reliable and unbiased measure of model quality, and that the autoregressive conditioning on prior samples plus speaker identity suffices to capture speaker characteristics without further mechanisms.

C3one line summary

WaveNet generates realistic raw audio using an autoregressive neural network with dilated convolutions, achieving state-of-the-art naturalness in speech synthesis for English and Mandarin.

References

60 extracted · 60 resolved · 3 Pith anchors

[1] Vocaine the vocoder and applications is speech synthesis 2015

[2] Mixture density networks 1994

[3] Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs 2015 · arXiv:1412.7062

[4] The Vowel: I ts Nature and Structure 1942

[5] Remaking speech 1939

Formal links

2 machine-checked theorem links

Cited by

86 papers in Pith

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Linguistically Augmented Audio Speech Data (LinguAS)

Receipt and verification

First computed	2026-07-04T21:24:19.546961Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2cc7f0d4efa46477a4ce7ae842c7b6f9e490f8ae9ab187abb9dde3956f4b0cf8

Aliases

arxiv: 1609.03499 · arxiv_version: 1609.03499v2 · doi: 10.48550/arxiv.1609.03499 · pith_short_12: FTD7BVHPURSH · pith_short_16: FTD7BVHPURSHPJGO · pith_short_8: FTD7BVHP

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FTD7BVHPURSHPJGOPLUEFR5W7H \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2cc7f0d4efa46477a4ce7ae842c7b6f9e490f8ae9ab187abb9dde3956f4b0cf8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "380f5fa5f82ba1fede626c4be26bc5009cbd1223c74641fe4362f0c2c2d241c6",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.SD",
    "submitted_at": "2016-09-12T17:29:40Z",
    "title_canon_sha256": "8d37eeeb8476318de01ce3bec1fd40de17d70bf9e4d3a9d31e1137e341742445"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}