Paper Citation Record · LEDGER

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2509.09201.

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

pith.paper-citation-record.v1
2509.09201 v1

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measured 57 of 57 reference resolution

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

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External citation measurements

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Outbound references

Observation 418f8ce1-a402-44c1-90ce-25eb16f76ea9 · outbound

This paper cites DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners

Reference 1

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Observation 51ebac8a-437c-4215-9974-fe6f2ad2e220 · outbound

This paper cites an unresolved cited work.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Unresolved cited work

Reference 2

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Observation d8b17411-afa8-4976-9d98-db076111a33b · outbound

This paper cites Based on the differences in the physical generation mechanisms, it can be assumed that speech signal and background sound signal are mutually independent [31].

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Based on the differences in the physical generation mechanisms, it can be assumed that speech signal and background sound signal are mutually independent [31]

Reference 3

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Observation d759351c-a47f-49b0-ae0f-79cb43032097 · outbound

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Unresolved cited work

Reference 4

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Observation 72f25802-bf56-4d13-895e-b50624272a68 · outbound

This paper cites front-end separation + back-end processing.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners front-end separation + back-end processing

Reference 5

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Observation 767b99ed-9000-425b-bb18-2a081141b7e3 · outbound

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Unresolved cited work

Reference 6

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Observation d74fee62-8daa-43f8-866d-6477696a3bba · outbound

This paper cites The experimental results confirm that the representations are sufficiently disentangled, enabling controllable feature selection tailored to diverse downstream tasks.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners The experimental results confirm that the representations are sufficiently disentangled, enabling controllable feature selection tailored to diverse downstream tasks

Reference 7

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Observation 3e72c3ac-f75b-48d2-aed9-0dd507bb38bf · outbound

This paper cites AudioPaLM: A Large Language Model That Can Speak and Listen.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners AudioPaLM: A Large Language Model That Can Speak and Listen

Reference 8

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Observation a000a9fd-0dd0-4ee8-9670-f95db715c479 · outbound

This paper cites Audit: Audio editing by follow- ing instructions with latent diffusion models,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Audit: Audio editing by follow- ing instructions with latent diffusion models,

Reference 9

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This paper cites Superm2m: Supervised and mixture-to-mixture co-learning for speech enhancement and noise-robust asr,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Superm2m: Supervised and mixture-to-mixture co-learning for speech enhancement and noise-robust asr,

Reference 10

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Observation 8c819585-5800-4c7e-8115-d2922fb1edba · outbound

This paper cites Preserving background sound in noise-robust voice conversion via multi-task learning,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Preserving background sound in noise-robust voice conversion via multi-task learning,

Reference 11

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Observation c2c04314-f074-4cb2-bc94-9e63231eff77 · outbound

This paper cites Supervised speech separation based on deep learning: An overview,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Supervised speech separation based on deep learning: An overview,

Reference 12

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Observation 675636fe-b5ad-4936-ae5f-1fda4a9b4368 · outbound

This paper cites Complex spectral mapping for single-and multi-channel speech enhancement and robust asr,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Complex spectral mapping for single-and multi-channel speech enhancement and robust asr,

Reference 13

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Observation 7636640a-1529-434f-8adf-e9212ee8de43 · outbound

This paper cites Speech enhancement with lstm recurrent neural networks and its application to noise- robust asr,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Speech enhancement with lstm recurrent neural networks and its application to noise- robust asr,

Reference 14

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This paper cites Deep neural networks for speech enhancement and speech recognition: A systematic review,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Deep neural networks for speech enhancement and speech recognition: A systematic review,

Reference 15

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Observation 8e8b811f-e6a9-42c5-adea-5d923a969839 · outbound

This paper cites Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement,

Reference 16

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Observation 0b3eaef0-d7cd-412e-89ea-c3cb7eabf06b · outbound

This paper cites Phonetic feature encoding in human superior temporal gyrus,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Phonetic feature encoding in human superior temporal gyrus,

Reference 17

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Observation 304a0721-9c83-40a0-acce-11fd71c31963 · outbound

This paper cites Representation of temporal sound features in the human auditory cortex,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Representation of temporal sound features in the human auditory cortex,

Reference 18

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Observation 288879da-e5ce-4acf-9ef9-f8d101a3c88a · outbound

This paper cites High Fidelity Neural Audio Compression.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners High Fidelity Neural Audio Compression

Reference 19

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Observation a70cc88a-458a-4e35-bd7a-f1b01a6c237e · outbound

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners High-fidelity au- dio compression with improved rvqgan,

Reference 20

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Observation 143f1e17-f16f-4f2d-b198-ec8a76fb5623 · outbound

This paper cites UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook

Reference 21

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Observation 7dfd02ad-5cab-496b-9e6d-33fe1fbad1e6 · outbound

This paper cites NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models

Reference 22

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Observation 9d390e1d-3709-4354-a1b9-ec425eae07b5 · outbound

This paper cites SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models

Reference 23

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Observation d2aa8661-24bb-4b08-835d-a182292dc5d0 · outbound

This paper cites Moshi: a speech-text foundation model for real-time dialogue.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Moshi: a speech-text foundation model for real-time dialogue

Reference 24

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Observation bfb54c71-31ca-47cd-bcd4-8373d8efd4c3 · outbound

This paper cites Dualcodec: A low-frame-rate, semantically-enhanced neural audio codec for speech generation,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Dualcodec: A low-frame-rate, semantically-enhanced neural audio codec for speech generation,

Reference 25

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Observation 48c2eb87-df7c-4182-adba-3d8e8ad3215a · outbound

This paper cites Brain plasticity under early auditory deprivation: evidence from congenital hearing-impaired people,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Brain plasticity under early auditory deprivation: evidence from congenital hearing-impaired people,

Reference 26

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Observation f3acf3a7-62b6-4c28-9b30-641c07a98d23 · outbound

This paper cites A tutorial on mpeg/audio compression,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners A tutorial on mpeg/audio compression,

Reference 27

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This paper cites Soundstream: An end-to-end neural audio codec,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Soundstream: An end-to-end neural audio codec,

Reference 28

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This paper cites HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec

Reference 29

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This paper cites Flow-vae vc: end-to-end flow framework with contrastive loss for zero-shot voice conversion,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Flow-vae vc: end-to-end flow framework with contrastive loss for zero-shot voice conversion,

Reference 30

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Hubert: Self-supervised speech rep- resentation learning by masked prediction of hidden units,

Reference 31

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 32

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Wavlm: Large-scale self-supervised pre-training for full stack speech processing,

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This paper cites SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities

Reference 34

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners RepCodec: A Speech Representation Codec for Speech Tokenization

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Funcodec: A fundamental, reproducible and integrable open-source toolkit for neural speech codec,

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This paper cites Semanticodec: An ul- tra low bitrate semantic audio codec for general sound,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Semanticodec: An ul- tra low bitrate semantic audio codec for general sound,

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This paper cites Sixty years of frequency-domain monaural speech en- hancement: From traditional to deep learning methods,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Sixty years of frequency-domain monaural speech en- hancement: From traditional to deep learning methods,

Reference 38

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This paper cites A review of vector quantization techniques,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners A review of vector quantization techniques,

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Unresolved cited work

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Neural dis- crete representation learning,

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

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This paper cites The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners The design for the wall street journal- based,

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This paper cites Esc: Dataset for environmental sound classification,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Esc: Dataset for environmental sound classification,

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

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This paper cites First stereo audio source separation evaluation campaign: data, algorithms and results,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners First stereo audio source separation evaluation campaign: data, algorithms and results,

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This paper cites Robust speech recognition via large-scale weak supervision,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Robust speech recognition via large-scale weak supervision,

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This paper cites Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

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This paper cites Inter-subnet: Speech enhancement with subband in- teraction,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Inter-subnet: Speech enhancement with subband in- teraction,

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This paper cites Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,

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DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Selm: Speech enhancement using discrete tokens and language mod- els,

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Observation 5ca5a13c-4aa2-47f2-825c-8cc2979366aa · outbound

This paper cites Attention is all you need,.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Attention is all you need,

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Observation 34b8bdc9-f242-4121-ac11-2ed83fb76510 · outbound

This paper cites PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T19:37:53.886688Z

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Observation f489a456-69c3-44f8-9163-9a4a535cf71a · outbound

This paper cites Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 56

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Observation c6eee5b8-c5b0-4fd9-b5d5-aeff362fc939 · outbound

This paper cites Decoupled Weight Decay Regularization.

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners Decoupled Weight Decay Regularization

Reference 57

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Observation 418f8ce1-a402-44c1-90ce-25eb16f76ea9 · inbound