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Paper Citation Record · LEDGER

SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1612.07837.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1612.07837 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T08:35:53.344189Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-05-25T11:35:43.708999Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1eff1d13-41a4-4ed2-b77f-8fe2d69247b0 · inbound

Generating Long Sequences with Sparse Transformers cites this paper.

Generating Long Sequences with Sparse Transformers SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-10T19:50:53.442406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-10T19:50:53.385943Z digest=sha256:1c73b1b7b34212eddca8b3f44d1129a9f9a894c5781415899109070496b27849

Observation 7d0518be-ce62-4536-a997-634c51084541 · inbound

Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding cites this paper.

Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-05-25T11:35:43.711785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-25T11:31:31.494334Z digest=sha256:0f9aab1a6f9ed148dfc6ac818d59098161c53bf2316652331834bcb3e1f904fa

Observation 93df6db1-2359-41da-916a-72a38df10713 · inbound

Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion cites this paper.

Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-05-24T14:54:36.416959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-24T14:51:52.781664Z digest=sha256:560fa6594b4c320d6405ef91a0affdaf8e9df52821422c0df0b7b66082d9a353

Observation cfd256db-dacc-4e61-a2e8-89d115778306 · inbound

Deep Time Series Models: A Comprehensive Survey and Benchmark cites this paper.

Deep Time Series Models: A Comprehensive Survey and Benchmark SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Reference 104

Resolution
verified exact
local_arxiv, observed 2026-05-23T23:05:51.548083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-23T23:03:45.096751Z digest=sha256:346a0a27bbe7b7e0f41ecd6d7bee4d7ad2c115d60de8a16a987b1ec3bcdf4dbc

Observation 140b6f0b-746a-4edd-8ed0-f123fdcfb7f8 · inbound

Text-To-Speech with Chain-of-Details: modeling temporal dynamics in speech generation cites this paper.

Text-To-Speech with Chain-of-Details: modeling temporal dynamics in speech generation SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-10T01:04:50.120499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-10T01:01:06.094276Z digest=sha256:94258905ca38f073d03f35221720472eaf6124a4d8529ce82e00f3dcf467e10c

Observation 025cbd59-2e49-4489-9820-34bced213633 · inbound

Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens cites this paper.

Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-03T08:35:53.344189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:35:53.344189Z digest=sha256:e0a6b171f7204dbe7c1237d31fa1ad518d2ae55ccc2966e9d5479d42a5e1bcf6