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Paper Citation Record · LEDGER

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding

As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2607.24323.

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

pith.paper-citation-record.v1
2607.24323 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T18:09:51.065507Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T18:09:50.397590Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved36
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbabebf4-d898-42b9-9f15-9da5b725c5d8 · outbound

This paper cites They play a key role in speech synthesis, such as in voice conversion and speaker anonymization systems [1, 2].

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding They play a key role in speech synthesis, such as in voice conversion and speaker anonymization systems [1, 2]

Reference 1

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Observation 5209250e-d653-46fc-af09-d8351e72b7f4 · outbound

This paper cites Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding

Reference 2

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source=pdf_text observed=2026-07-31T18:09:50.397590Z digest=sha256:f8dc8e4c025ed7ed022c081b77f734828f1116cc2611537871d5aab17be39d84

Observation 791c9053-d405-4802-bc8b-82343483a2b2 · outbound

This paper cites an unresolved cited work.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Unresolved cited work

Reference 3

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Observation ce1e616f-4106-4413-9e82-7d366c67e624 · outbound

This paper cites Datasets For our trainings, we used LibriTTS [27] subsets (train-clean-100, train-clean-360, train-other-500), similar to BigVGAN and V ocos [3, 6].

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Datasets For our trainings, we used LibriTTS [27] subsets (train-clean-100, train-clean-360, train-other-500), similar to BigVGAN and V ocos [3, 6]

Reference 4

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source=pdf_text observed=2026-07-31T18:09:50.539665Z digest=sha256:a60431b5778a7f345870794b051746fbed1ffca397e655047d448c947ead2dc5

Observation 1ee9fef3-de81-488e-938c-df9434ac0d40 · outbound

This paper cites an unresolved cited work.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Unresolved cited work

Reference 5

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source=pdf_text observed=2026-07-31T18:09:50.623116Z digest=sha256:bc1e9456a86e263eeb75e4de75915c5e6a81538e8f317e6dbd645efd3dc00003

Observation c1127f58-7893-4fae-a8a2-a35440b356ea · outbound

This paper cites The results, reported in the bot- tom rows of Table 2, highlight that V ocos-Mag outperforms almost any other experiment in this paper, while V ocos-Phase performs the worst.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding The results, reported in the bot- tom rows of Table 2, highlight that V ocos-Mag outperforms almost any other experiment in this paper, while V ocos-Phase performs the worst

Reference 6

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source=pdf_text observed=2026-07-31T18:09:50.710272Z digest=sha256:9779140cecea669a2d09cfec4619ed0271b0cec99c6aabb4b849b860953a6aa2

Observation cecb3492-20c5-4563-bab3-63cc8fc908ea · outbound

This paper cites not a bug, but a feature.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding not a bug, but a feature

Reference 7

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source=pdf_text observed=2026-07-31T18:09:50.807124Z digest=sha256:ab568b4d5227332b1bfb2782291adfc3a4273ed3a1a9fa0a86492f8abd5f4159

Observation 1584c7a9-6c23-419f-9d5d-381ebbd1f827 · outbound

This paper cites How should we extract discrete audio tokens from self-supervised models?.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding How should we extract discrete audio tokens from self-supervised models?

Reference 8

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source=pdf_text observed=2026-07-31T18:09:50.888769Z digest=sha256:6342b3dccabeadc2a1d854b25b77dea2955e42bb19cd5284bc9fbf65b2536c3f

Observation b555ceaf-201f-4734-b28b-b27adff4f809 · outbound

This paper cites Why disentanglement- based speaker anonymization systems fail at preserving emo- tions?.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Why disentanglement- based speaker anonymization systems fail at preserving emo- tions?

Reference 9

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source=pdf_text observed=2026-07-31T18:09:50.957953Z digest=sha256:b5fd203b71f2c0698223094dd8229336639a0101002cf62ec87dd038737a06e3

Observation 9469bfd7-1a98-4415-864e-7155575e51cb · outbound

This paper cites BigVGAN: A universal neural vocoder with large-scale training,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding BigVGAN: A universal neural vocoder with large-scale training,

Reference 10

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source=pdf_text observed=2026-07-31T18:09:50.962206Z digest=sha256:532efaa435ebe0475af5649e2f1c7b8404ac52a64d1816d773dc469e35b996df

Observation 3f7cf53c-aad0-4b93-9dba-affa46b3ca30 · outbound

This paper cites A streamwise GAN vocoder for wideband speech coding at very low bit rate,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding A streamwise GAN vocoder for wideband speech coding at very low bit rate,

Reference 11

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source=pdf_text observed=2026-07-31T18:09:50.966452Z digest=sha256:927ae9d920719a3c6378a48cefb9f04b199e860d7f074c27a5870052f74f7cf0

Observation d18aa35e-2e41-4ff3-a5a9-c41c87a47778 · outbound

This paper cites Lightweight and high-fidelity end-to-end text-to-speech with multi-band generation and inverse short-time Fourier transform,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Lightweight and high-fidelity end-to-end text-to-speech with multi-band generation and inverse short-time Fourier transform,

Reference 12

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source=pdf_text observed=2026-07-31T18:09:50.970491Z digest=sha256:3c2a65991688306dde500f398113a2c241852087701f2d64e2048b8af55f3eac

Observation 5a3ba894-b7d9-4001-ae47-bdd82ce0a65f · outbound

This paper cites V ocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding V ocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis,

Reference 13

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source=pdf_text observed=2026-07-31T18:09:50.974999Z digest=sha256:ba156153fb2417709ae883fcdb2162c0652cad00fae113188e8f3500af78af23

Observation e07aa6db-3cc1-4121-a499-82213eb85c95 · outbound

This paper cites WaveNeXt: ConvNeXt-based fast neural vocoder without ISTFT layer,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding WaveNeXt: ConvNeXt-based fast neural vocoder without ISTFT layer,

Reference 14

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source=pdf_text observed=2026-07-31T18:09:50.978461Z digest=sha256:3ba854f513c330b4138ec955e77da0eb97c74b9cf7bb919b52667617b2985216

Observation ae61b670-08f2-4623-ad5d-4210f1ac1738 · outbound

This paper cites Phase-vocoder: About this phasi- ness business,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Phase-vocoder: About this phasi- ness business,

Reference 15

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source=pdf_text observed=2026-07-31T18:09:50.982225Z digest=sha256:3e3055de4de5143b0a3b21434d8355769e19dbe1d2bd5b69ef67b6174746a0df

Observation 23a6fed3-26cd-4808-9446-a17f063b6b0d · outbound

This paper cites Chunked autoregressive GAN for condi- tional waveform synthesis,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Chunked autoregressive GAN for condi- tional waveform synthesis,

Reference 16

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source=pdf_text observed=2026-07-31T18:09:50.985679Z digest=sha256:023438549b260ebe37c78f409f21f2396e7946c449e0fcebf92503cbe57c0d42

Observation a2dd9e85-f6da-472e-b516-b42055f1ac88 · outbound

This paper cites Wavehax: Aliasing-free neural waveform synthesis based on 2d convolution and harmonic prior for reliable complex spectrogram estimation,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Wavehax: Aliasing-free neural waveform synthesis based on 2d convolution and harmonic prior for reliable complex spectrogram estimation,

Reference 17

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source=pdf_text observed=2026-07-31T18:09:50.989107Z digest=sha256:9c1ef8a0d05f08d9a68de9dcd7edf3e40cf9e76f4adf6b60288fbdbc24af0529

Observation 7ff4e064-92dd-485d-ad44-895e286f7eee · outbound

This paper cites Signal estimation from modified short-time Fourier transform,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Signal estimation from modified short-time Fourier transform,

Reference 18

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Observation 3caa0ea2-4aa4-465c-8891-8c09bbada452 · outbound

This paper cites FreeV: Free lunch for vocoders through pseudo inversed mel filter,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding FreeV: Free lunch for vocoders through pseudo inversed mel filter,

Reference 19

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source=pdf_text observed=2026-07-31T18:09:50.995943Z digest=sha256:97ffc5592a088803f056fcefb0970daaf7106e5ce3e81f4a27ecadc3e5b7ea13

Observation 9f652638-ceb5-423d-8503-dd080e84c37d · outbound

This paper cites Is GAN necessary for mel-spectrogram-based neural vocoder?.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Is GAN necessary for mel-spectrogram-based neural vocoder?

Reference 20

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Observation c0362bfc-c3d0-4ae9-8aca-18d7e38f6645 · outbound

This paper cites Learning neural vocoder from range-null space decomposition,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Learning neural vocoder from range-null space decomposition,

Reference 21

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source=pdf_text observed=2026-07-31T18:09:51.003460Z digest=sha256:0cceb7fc3ece0e109ea00294f0f676e889c982d8cccfea91b54da0d8b89677d1

Observation e22b716f-f82b-4e33-b140-e4c24fd781f5 · outbound

This paper cites A ConvNet for the 2020s,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding A ConvNet for the 2020s,

Reference 22

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Observation e5210d1f-5bf0-49ed-af12-84d56046f728 · outbound

This paper cites High-fidelity audio compression with improved RVQGAN,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding High-fidelity audio compression with improved RVQGAN,

Reference 23

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source=pdf_text observed=2026-07-31T18:09:51.011123Z digest=sha256:23021b08a54b21396c01509f4cdd85ebd485a7a9a4108b7dc3e96bae36b23256

Observation 6c931f07-3bc6-4565-9f1e-2f0f78aaef08 · outbound

This paper cites High fi- delity neural audio compression,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding High fi- delity neural audio compression,

Reference 24

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Observation 3652aa80-0d1e-4b9c-afb2-586ac1c7f0b6 · outbound

This paper cites Multi-scale sub- band constant-q transform discriminator for high-fidelity vocoder,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Multi-scale sub- band constant-q transform discriminator for high-fidelity vocoder,

Reference 25

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Observation 68fda27e-a2f7-424e-8212-b94f2cf19d2d · outbound

This paper cites On phase- magnitude relationships in the short-time Fourier transform,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding On phase- magnitude relationships in the short-time Fourier transform,

Reference 26

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Observation be4ce4a2-e7f8-4ec4-9b4f-a10d3373f3de · outbound

This paper cites A noniterative method for reconstruction of phase from STFT magnitude,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding A noniterative method for reconstruction of phase from STFT magnitude,

Reference 27

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source=pdf_text observed=2026-07-31T18:09:51.025454Z digest=sha256:00e15e31444fd693baadedbdffd064f1342e2f62f5cd3a4cee6a3f46d8af36a3

Observation 7b705d3c-6f79-4bed-b6a0-b0df0f267072 · outbound

This paper cites Adversarial generation of time-frequency features with ap- plication in audio synthesis,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Adversarial generation of time-frequency features with ap- plication in audio synthesis,

Reference 28

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source=pdf_text observed=2026-07-31T18:09:51.029061Z digest=sha256:de0ea1cef5b3660ab8c490ffd339ee2155741f8a945fc9cea5577988a204d80a

Observation 89b19adb-4edf-436f-b2c2-67617e828977 · outbound

This paper cites Mel spectrogram in- version with stable pitch,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Mel spectrogram in- version with stable pitch,

Reference 29

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source=pdf_text observed=2026-07-31T18:09:51.033402Z digest=sha256:1c056ea3aea46e4c97b546da4cf68ef2311083baa67d5f0f9789cf6872f864b5

Observation e870f822-db8d-4612-85a9-a986be067233 · outbound

This paper cites Online phase reconstruction via DNN-based phase differ- ences estimation,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Online phase reconstruction via DNN-based phase differ- ences estimation,

Reference 30

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source=pdf_text observed=2026-07-31T18:09:51.036739Z digest=sha256:62fcc488106a5a141515fdfb022f7f891dce5be56f9606fda1d1fd305afb97f9

Observation 1644b215-3130-43de-9d2e-232ddb4a4aab · outbound

This paper cites Efficient neural and numerical methods for high-quality on- line speech spectrogram inversion via gradient theorem,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Efficient neural and numerical methods for high-quality on- line speech spectrogram inversion via gradient theorem,

Reference 31

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source=pdf_text observed=2026-07-31T18:09:51.040713Z digest=sha256:6ec4b342809efdc87de90352d290ce13c52973da58913864d69f7ece8c49c6c9

Observation fddb1083-3750-4256-8095-5086da14f7e7 · outbound

This paper cites STFT phase reconstruc- tion in voiced speech for an improved single-channel speech enhancement,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding STFT phase reconstruc- tion in voiced speech for an improved single-channel speech enhancement,

Reference 32

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source=pdf_text observed=2026-07-31T18:09:51.044447Z digest=sha256:17949ec33740e0a39f919aef1dbc58bdf6ee08b6aeb4e75df918ade417bed3aa

Observation 15b4aeaf-850f-4031-a722-9965eeb30553 · outbound

This paper cites Low-latency neural speech phase pre- diction based on parallel estimation architecture and anti- wrapping losses for speech generation tasks,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Low-latency neural speech phase pre- diction based on parallel estimation architecture and anti- wrapping losses for speech generation tasks,

Reference 33

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source=pdf_text observed=2026-07-31T18:09:51.047967Z digest=sha256:18b5b2a03ed61566733797d525e5d39214e048d2cc6aba18d9951817541001dc

Observation 1f07031d-5317-4f50-9c10-bed8be8490a3 · outbound

This paper cites LibriTTS: A corpus derived from LibriSpeech for text-to-speech,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding LibriTTS: A corpus derived from LibriSpeech for text-to-speech,

Reference 34

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source=pdf_text observed=2026-07-31T18:09:51.051386Z digest=sha256:e6196d1b2f4862f543e49081292d6c8321a46fc5f35fe7c4849cd117ebc0a3db

Observation 9a694100-93a9-4d90-8f69-6aedf4c9fe07 · outbound

This paper cites SCOREQ: Speech quality assessment with contrastive regression,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding SCOREQ: Speech quality assessment with contrastive regression,

Reference 35

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source=pdf_text observed=2026-07-31T18:09:51.054655Z digest=sha256:0ff6cf43aa11bcc7a77c3cda6692a11e2c4bf7525f6da79f327e0153b42494e0

Observation 321f1128-d2b3-40c1-93cf-31bdcbdf42e8 · outbound

This paper cites webMUSHRA — a comprehensive framework for web-based listening tests,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding webMUSHRA — a comprehensive framework for web-based listening tests,

Reference 36

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no resolver link, observed 2026-07-31T18:09:51.058376Z

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source=pdf_text observed=2026-07-31T18:09:51.058376Z digest=sha256:8d66b8182f6bbf2543aef59acdd3cf111e2b8dad3580b11f2ad4d22dec38f6af

Observation 630ce190-b480-407f-8386-e75059294d8f · outbound

This paper cites A com- parison of recent neural vocoders for speech signal recon- struction,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding A com- parison of recent neural vocoders for speech signal recon- struction,

Reference 37

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source=pdf_text observed=2026-07-31T18:09:51.062202Z digest=sha256:3a1f257e371fdd7e68e3e49ec572516af75c1a8fbeced834871618ada59f8605

Observation 5d38dc6b-72dd-490f-9e89-25bab4e65b1a · outbound

This paper cites APNet2: High- quality and high-efficiency neural vocoder with direct predic- tion of amplitude and phase spectra,.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding APNet2: High- quality and high-efficiency neural vocoder with direct predic- tion of amplitude and phase spectra,

Reference 38

Resolution
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no resolver link, observed 2026-07-31T18:09:51.065507Z

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source=pdf_text observed=2026-07-31T18:09:51.065507Z digest=sha256:336380c504519467f7a98a35dc1d6d49fa8b37fe42282933fb577d680af90cc2

Pith citing papers

Observation 5209250e-d653-46fc-af09-d8351e72b7f4 · inbound

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding cites this paper.

Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding

Reference 2

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no resolver link, observed 2026-07-31T18:09:50.397590Z

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source=pdf_text observed=2026-07-31T18:09:50.397590Z digest=sha256:f8dc8e4c025ed7ed022c081b77f734828f1116cc2611537871d5aab17be39d84