Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement | Paper Citation Record

Observation fb452aaa-77cf-4b3e-ac7c-42db3d726277 · outbound

This paper cites Suppression of acoustic noise in speech using spectral subtraction,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Suppression of acoustic noise in speech using spectral subtraction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.790354Z

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Observation b8ecd159-1100-4b05-8c0f-131ccbb33909 · outbound

This paper cites Speech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Speech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.759370Z

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Observation ac5121c3-340e-451c-b8bc-5d4373dbd99d · outbound

This paper cites Noise spectrum estimation in adverse environments: Im- proved minima controlled recursive averaging,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Noise spectrum estimation in adverse environments: Im- proved minima controlled recursive averaging,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.731699Z

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Observation 2248bc47-9db4-4318-9e79-65493a1368fc · outbound

This paper cites Speech enhancement for non-stationary noise environments,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Speech enhancement for non-stationary noise environments,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.694044Z

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Observation bc4faf1a-58aa-4dd9-a017-8900d45786c7 · outbound

This paper cites DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.655859Z

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Observation e4a27e43-6133-4e89-ba72-c7395fcd7c45 · outbound

This paper cites A mask free neural network for monaural speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement A mask free neural network for monaural speech enhancement,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.617811Z

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Observation 7b849d20-0556-42b8-b537-2438683dec43 · outbound

This paper cites DeepFil- ternet2: Towards real-time speech enhancement on embedded devices for full-band audio,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement DeepFil- ternet2: Towards real-time speech enhancement on embedded devices for full-band audio,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.586176Z

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Observation f1030fe7-f696-49e0-8749-a60529f2aa22 · outbound

This paper cites Real-time denoising and dereverberation with tiny recurrent U-Net,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Real-time denoising and dereverberation with tiny recurrent U-Net,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.557374Z

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Observation b6d41615-4770-4bae-ac17-bdfa732580ff · outbound

This paper cites FRCRN: Boosting feature representation using frequency recurrence for monaural speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement FRCRN: Boosting feature representation using frequency recurrence for monaural speech enhancement,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.534578Z

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Observation 53d01902-03f8-4f35-8e7e-7385c6274eba · outbound

This paper cites Ultra low complexity deep learning based noise suppression,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Ultra low complexity deep learning based noise suppression,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.503471Z

Source-reported events for the cited work

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Observation 88a282a8-f433-40f1-be82-b25360ac45dc · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Supervised speech separation based on deep learning: An overview,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.480039Z

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Observation ddf672e4-6b32-457f-b2a8-0cc161769a70 · outbound

This paper cites Tasnet: time-domain audio separation network for real-time, single-channel speech separation,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Tasnet: time-domain audio separation network for real-time, single-channel speech separation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.446224Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a56699de-c3d6-49c6-b50c-cd197b621dcc · outbound

This paper cites The InterSpeech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement The InterSpeech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.419952Z

Source-reported events for the cited work

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Observation 8be47e6b-812b-4444-a15b-9905d4d40614 · outbound

This paper cites Investigat- ing RNN-based speech enhancement methods for noise-robust text-to- speech,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Investigat- ing RNN-based speech enhancement methods for noise-robust text-to- speech,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.385390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71abab93-8e83-4743-b4c4-13a1adc077dc · outbound

This paper cites Masking and inpainting: A two-stage speech enhancement approach for low SNR and non-stationary noise,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Masking and inpainting: A two-stage speech enhancement approach for low SNR and non-stationary noise,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.361854Z

Source-reported events for the cited work

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Observation db1a38e0-d12c-44c7-af98-3150c1f657b0 · outbound

This paper cites Comparative analysis of discriminative deep learning-based noise reduction methods in low SNR scenarios,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Comparative analysis of discriminative deep learning-based noise reduction methods in low SNR scenarios,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.331022Z

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Observation d895120e-637a-44ea-8b58-0af275a33fbb · outbound

This paper cites SEGAN: Speech enhancement generative adversarial network,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement SEGAN: Speech enhancement generative adversarial network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.305703Z

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Observation 34f722fc-d378-4d37-a1d6-b392237b17dc · outbound

This paper cites MetricGAN+: An improved version of MetricGAN for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement MetricGAN+: An improved version of MetricGAN for speech enhancement,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.248422Z

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Observation 01bdc94f-cb98-42e4-8db4-4682ee97dd9f · outbound

This paper cites CMGAN: Conformer-based metric GAN for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement CMGAN: Conformer-based metric GAN for speech enhancement,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.223012Z

Source-reported events for the cited work

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Observation 1f1a1e47-fa39-4620-b1db-0c152ab015cb · outbound

This paper cites SEFGAN: Harvesting the power of normalizing flows and GANs for efficient high-quality speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement SEFGAN: Harvesting the power of normalizing flows and GANs for efficient high-quality speech enhancement,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.184485Z

Source-reported events for the cited work

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Observation 4418a381-28ac-4e5b-9070-8f32a7da6d05 · outbound

This paper cites TFDense-GAN: a generative adversarial network for single-channel speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement TFDense-GAN: a generative adversarial network for single-channel speech enhancement,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.153592Z

Source-reported events for the cited work

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Observation 671f8f7e-4765-4d7a-acbb-731cfa0eed52 · outbound

This paper cites A comprehensive review on generative models for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement A comprehensive review on generative models for speech enhancement,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.126289Z

Source-reported events for the cited work

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Observation 94885140-2c26-43e9-af8c-48e447b372f2 · outbound

This paper cites Speech enhancement and dereverberation with diffusion-based genera- tive models,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Speech enhancement and dereverberation with diffusion-based genera- tive models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.096089Z

Source-reported events for the cited work

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Observation 99e4cbe4-5630-4411-9cac-58d46cec50b6 · outbound

This paper cites Conditional diffusion probabilistic model for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Conditional diffusion probabilistic model for speech enhancement,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.061168Z

Source-reported events for the cited work

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Observation 4a35c158-b7aa-4b11-87c7-8a9eb85714d5 · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement StoRM: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:50.021467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a56c313e-dcf5-4136-a9d6-63ec6bc309b6 · outbound

This paper cites Cold diffusion for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Cold diffusion for speech enhancement,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.981736Z

Source-reported events for the cited work

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Observation 9ae57caa-4de3-46cc-8897-3a9d902d5c6e · outbound

This paper cites Conditional latent diffusion-based speech enhancement via dual context learning,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Conditional latent diffusion-based speech enhancement via dual context learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.944889Z

Source-reported events for the cited work

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Observation e201143c-8d06-449a-a9ff-9ac5b9cdac20 · outbound

This paper cites Universal score- based speech enhancement with high content preservation,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Universal score- based speech enhancement with high content preservation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.912325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ab4c9bb3-e247-4d25-b04e-27533a2d57e0 · outbound

This paper cites Cross-domain diffusion based speech enhance- ment for very noisy speech,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Cross-domain diffusion based speech enhance- ment for very noisy speech,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.886357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c6a195c-1d1a-424d-9537-c3b35618d56b · outbound

This paper cites ICASSP 2024 speech signal improvement challenge,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement ICASSP 2024 speech signal improvement challenge,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.856998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 463bd107-15a2-4715-9e65-ff80146667b2 · outbound

This paper cites General speech restoration using two-stage generative adversarial networks,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement General speech restoration using two-stage generative adversarial networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.833841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 669ad40e-279e-412f-84d5-66cd1ab4d5b6 · outbound

This paper cites KS-Net: Multi-band joint speech restoration and enhancement network,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement KS-Net: Multi-band joint speech restoration and enhancement network,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.775386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39b99463-7573-47a8-9630-a49abc394552 · outbound

This paper cites Renet: A time-frequency domain general speech restoration network,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Renet: A time-frequency domain general speech restoration network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.565062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a33e934-3c9d-4963-a552-beb71ec5e2e6 · outbound

This paper cites Generative adversarial network-based postfilter for STFT spectrograms,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Generative adversarial network-based postfilter for STFT spectrograms,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.332019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1263a039-98fc-4fd6-9cba-53b30f7de5bd · outbound

This paper cites PostGAN: A gan-based post-processor to enhance the quality of coded speech,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement PostGAN: A gan-based post-processor to enhance the quality of coded speech,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:49.121433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 197fbee4-4cea-4737-83b0-922f84157517 · outbound

This paper cites DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T14:48:36.957685Z

Source-reported events for the cited work

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Observation d46aa56a-0afd-4677-a2ea-1888dfe791bf · outbound

This paper cites GAN-based speech enhancement for low SNR using latent feature conditioning,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement GAN-based speech enhancement for low SNR using latent feature conditioning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:48.896296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e358cec-ec90-484e-89a0-3b16b33d8486 · outbound

This paper cites SEANet: A multi- modal speech enhancement network,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement SEANet: A multi- modal speech enhancement network,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:48.630654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87c7f524-8281-489f-b613-70eab8231265 · outbound

This paper cites FunCodec: A fundamental, reproducible and integrable open-source toolkit for neural speech codec,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement FunCodec: A fundamental, reproducible and integrable open-source toolkit for neural speech codec,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:48.401427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b94178c7-563d-40c5-9b5c-49ce51c99d93 · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Image-to-image translation with conditional adversarial networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:48.224499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e64c83d9-0bd5-4c54-8c46-f7e0420d9c8c · outbound

This paper cites MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:48.066631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9e05a41-d975-45da-b471-81f09b85b749 · outbound

This paper cites Generative adversarial network-based postfilter for sta- tistical parametric speech synthesis,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Generative adversarial network-based postfilter for sta- tistical parametric speech synthesis,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:47.832636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 237f4502-56cb-41f8-90b1-45239e6a06ce · outbound

This paper cites Improving the naturalness of synthesized spectrograms for TTS using ganbased post-processing,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Improving the naturalness of synthesized spectrograms for TTS using ganbased post-processing,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:47.607742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b8d1101-acca-432c-b410-2c8df768aa19 · outbound

This paper cites Goodfellow, Y.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Goodfellow, Y

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:47.381089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ebf59dac-1cd8-4ae6-86c5-ae2c757a6c81 · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement FiLM: Visual reasoning with a general conditioning layer,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:47.124829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b098a42e-8967-419d-b122-263b1bda964f · outbound

This paper cites an unresolved cited work.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:48:46.891010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9495e35e-ecf7-41b8-8350-db8a48f45ddd · outbound

This paper cites Attention is all you need,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Attention is all you need,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:46.636023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f00410f9-437c-4be9-999d-80ad1484a7db · outbound

This paper cites Densely connected neural network with dilated convolutions for real-time speech enhancement in the time domain,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Densely connected neural network with dilated convolutions for real-time speech enhancement in the time domain,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:46.367339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c3be6545-ff75-4a65-81be-e20b249911bc · outbound

This paper cites Taylor, can you hear me now? a Taylor-unfolding framework for monaural speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Taylor, can you hear me now? a Taylor-unfolding framework for monaural speech enhancement,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:46.142097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation adc073a6-0fa4-48bc-972d-f85da5c93b00 · outbound

This paper cites Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:45.948748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77a70348-ec02-4756-aaf1-822ce1738b64 · outbound

This paper cites Conditional image generation with pixelcnn decoders,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Conditional image generation with pixelcnn decoders,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:45.723882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8f62ecd-b7b6-46fd-bccc-4f1c0ef47614 · outbound

This paper cites High fidelity neural audio compression,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement High fidelity neural audio compression,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:45.489734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d312416-8b36-4d23-8441-fa1a96f6d18a · outbound

This paper cites The diverse environments multi- channel acoustic noise database (demand): A database of multichannel environmental noise recordings,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement The diverse environments multi- channel acoustic noise database (demand): A database of multichannel environmental noise recordings,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:45.258107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7fe5704-5532-4713-8ab1-47fd9a14f1b1 · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement ESC: Dataset for environmental sound classification,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:45.029655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3baa0f40-96e7-4c16-a8f8-317f10d65e95 · outbound

This paper cites A pitch tracking corpus with evaluation on multipitch tracking scenario,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement A pitch tracking corpus with evaluation on multipitch tracking scenario,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:44.795950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 95cd7150-986f-4cd3-84fa-341ed975c122 · outbound

This paper cites Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:44.560733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 393e6d8a-f9aa-462d-887c-ffc9b9c11230 · outbound

This paper cites Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:44.334240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 185f7c55-3743-4cb0-8f2c-6f39464ff75a · outbound

This paper cites SDR–half- baked or well done?,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement SDR–half- baked or well done?,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:44.099884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a5f36ceb-965b-4ea2-8e3e-cba4c2244ada · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Robust speech recognition via large-scale weak supervi- sion,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:43.866343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca4c848e-bc8f-4d1e-bdfb-3a3b40fb1700 · outbound

This paper cites From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition.,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition.,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:43.630029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1056a4c-9edc-4c7e-9f8e-f0d7c37e53e6 · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:43.396729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd6adcc0-a0c8-49d9-9ef7-5cf5428b8d8a · outbound

This paper cites An open source implementation of ITU-T recommendation p. 808 with validation,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement An open source implementation of ITU-T recommendation p. 808 with validation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:43.182288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5125a3c6-d87a-43d8-a9cf-5233bc62a49b · outbound

This paper cites DNSMOS P. 835: A non- intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement DNSMOS P. 835: A non- intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.944623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f07654e0-436d-4580-b45f-f1be5af12502 · outbound

This paper cites P. 835: Subjective test methodology for evaluating speech communication systems that include noise suppression algorithm,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement P. 835: Subjective test methodology for evaluating speech communication systems that include noise suppression algorithm,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.749020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 523afb1d-6bbd-42e7-937e-3ddc51300b8a · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement SCOREQ: Speech quality assessment with contrastive regression,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.672222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 70e96f76-7fc4-46a0-b8a0-8046938f7ed9 · outbound

This paper cites Evaluation of objective quality measures for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Evaluation of objective quality measures for speech enhancement,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.551102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9919303-3939-4921-a06d-1bd0218d0228 · outbound

This paper cites Method for the subjective assessment of intermediate quality level of audio systems,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Method for the subjective assessment of intermediate quality level of audio systems,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.414301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0558f3fb-f563-43d5-8d2c-ca753d8b1a0f · outbound

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

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement webMUSHRA—a comprehensive framework for web-based listening tests,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.275204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5fa85c48-6047-4a5a-b16a-efa5c956619b · outbound

This paper cites HiFi-GAN: High-fidelity denoising and dereverberation based on speech deep features in adversarial net- works,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement HiFi-GAN: High-fidelity denoising and dereverberation based on speech deep features in adversarial net- works,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:42.122047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 72c5742e-2835-4822-bd66-79f7c2c734e8 · outbound

This paper cites Speech enhancement with score-based generative models in the complex STFT domain,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Speech enhancement with score-based generative models in the complex STFT domain,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:41.899660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2f24a493-5cdb-4335-beed-02b16fe54a8b · outbound

This paper cites A recurrent variational autoencoder for speech enhancement,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement A recurrent variational autoencoder for speech enhancement,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:41.660286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50992f86-c761-4373-a255-584063061a6b · outbound

This paper cites Speech enhancement with score-based generative models in the complex STFT domain,.

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement Speech enhancement with score-based generative models in the complex STFT domain,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:48:41.386745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Reference resolution

Outbound references

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