DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement | Paper Citation Record

Observation 134aa10f-1665-417c-a0f3-443e2ca318f1 · outbound

This paper cites Validity and robustness of denoisers: A proof of concept in speech denoising,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Validity and robustness of denoisers: A proof of concept in speech denoising,

Reference 1

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Observation 4189d019-fe54-4b29-b796-72b339892e7d · outbound

This paper cites Dubbing movies via hierarchical phoneme modeling and acoustic diffusion denoising,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dubbing movies via hierarchical phoneme modeling and acoustic diffusion denoising,

Reference 2

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Observation 319db937-7e43-44af-9aed-f146b35e1446 · outbound

This paper cites Bsdb-net: Band-split dual-branch network with selective state spaces mechanism for monaural speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Bsdb-net: Band-split dual-branch network with selective state spaces mechanism for monaural speech enhancement,

Reference 3

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Observation 02f6518d-a283-4de3-984e-4925582e8e61 · outbound

This paper cites Cross-modal knowledge distillation with multi-stage adaptive feature fusion for speech separa- tion,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Cross-modal knowledge distillation with multi-stage adaptive feature fusion for speech separa- tion,

Reference 4

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Observation 9745b5ca-87e6-40f0-8cca-3b337a1c90bb · outbound

This paper cites Waveform-domain speech enhancement using spectrogram encoding for robust speech recognition,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Waveform-domain speech enhancement using spectrogram encoding for robust speech recognition,

Reference 5

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Observation 3786189b-1b84-4843-aa15-05f787c956b5 · outbound

This paper cites Automatic speech recognition: A survey of deep learning techniques and approaches,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Automatic speech recognition: A survey of deep learning techniques and approaches,

Reference 6

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Observation 6e516322-ca1e-4a97-ab80-ce6e3d022ff3 · outbound

This paper cites Seeing helps hearing: A multi-modal dataset and a mamba- based dual branch parallel network for auditory attention decoding,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Seeing helps hearing: A multi-modal dataset and a mamba- based dual branch parallel network for auditory attention decoding,

Reference 7

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Observation 45f15fb9-904f-4f24-a781-e60484b8025e · outbound

This paper cites An overview of deep-learning-based audio-visual speech en- hancement and separation,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement An overview of deep-learning-based audio-visual speech en- hancement and separation,

Reference 8

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Observation 5e192418-3f10-4cf0-b183-07d1b92c3e33 · outbound

This paper cites Sse-net: Towards low-power-consumption spiking neural network for monaural speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Sse-net: Towards low-power-consumption spiking neural network for monaural speech enhancement,

Reference 9

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Observation 3f639b45-7dac-40df-81c5-0932f9096acf · outbound

This paper cites AI Flow: Perspectives, Scenarios, and Approaches.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement AI Flow: Perspectives, Scenarios, and Approaches

Reference 10

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Observation ad392537-605b-44d1-ab08-7cf4bc5497b2 · outbound

This paper cites Compact deep neural networks for real-time speech enhancement on resource-limited devices,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Compact deep neural networks for real-time speech enhancement on resource-limited devices,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 05530556-2df9-42f2-8283-e09fa46440be · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dccrn: Deep complex convolution recurrent network for phase-aware speech enhancement,

Reference 12

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Observation 777a0908-5226-4261-bc91-7bb3efc91ed0 · outbound

This paper cites Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 7af71d61-3a68-49d2-a576-946cf5339cdc · outbound

This paper cites Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation d0d1f2a3-f3da-43b6-986a-866625b54c00 · outbound

This paper cites Two heads are better than one: A two-stage complex spectral mapping approach for monaural speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Two heads are better than one: A two-stage complex spectral mapping approach for monaural speech enhancement,

Reference 15

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Observation ea53361b-63a2-4e0a-a1da-1e58d4ad437c · outbound

This paper cites Dbt-net: Dual-branch federative magnitude and phase estimation with attention- in-attention transformer for monaural speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dbt-net: Dual-branch federative magnitude and phase estimation with attention- in-attention transformer for monaural speech enhancement,

Reference 16

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Observation 6a4fb0a2-b783-4375-ab3c-c82f25d99fec · outbound

This paper cites A convolutional recurrent neural network for real-time speech enhancement.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A convolutional recurrent neural network for real-time speech enhancement

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 7843751c-6a26-43e3-b0bb-fbf7ed16388d · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,

Reference 18

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Observation 036c8a71-fef0-410e-b4c3-3d7ca9d483e6 · outbound

This paper cites On the compensation between magnitude and phase in speech separation,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement On the compensation between magnitude and phase in speech separation,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 0c005e12-baa7-4a93-8c3d-5c79eb336b7b · outbound

This paper cites Seeing helps hearing: A multi-modal dataset and a mamba- based dual branch parallel network for auditory attention decoding,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Seeing helps hearing: A multi-modal dataset and a mamba- based dual branch parallel network for auditory attention decoding,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation d50412e6-2493-4ea3-93b4-d4f6c523b389 · outbound

This paper cites Glance and gaze: A collaborative learning framework for single-channel speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Glance and gaze: A collaborative learning framework for single-channel speech enhancement,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 8cc78dd9-19d4-49da-a229-d611329b2476 · outbound

This paper cites Fullsubnet: A full-band and sub- band fusion model for real-time single-channel speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Fullsubnet: A full-band and sub- band fusion model for real-time single-channel speech enhancement,

Reference 22

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Observation 2007b627-20c7-4504-96fd-8d505ff42fd1 · outbound

This paper cites A low-power streaming speech enhance- ment accelerator for edge devices,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A low-power streaming speech enhance- ment accelerator for edge devices,

Reference 23

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Observation ba34d46b-7907-4812-a2bd-6454d64099f7 · outbound

This paper cites Flowse: Flow matching- based speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Flowse: Flow matching- based speech enhancement,

Reference 24

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Observation 14dbc579-e443-4156-9f97-75241cc8d9a5 · outbound

This paper cites Toward ultralow- power neuromorphic speech enhancement with spiking-fullsubnet,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Toward ultralow- power neuromorphic speech enhancement with spiking-fullsubnet,

Reference 25

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Observation 0814e5cf-8f3b-444e-ab2d-940a32dfa586 · outbound

This paper cites Speech emotion recognition based on spiking neural network and convolutional neural network,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Speech emotion recognition based on spiking neural network and convolutional neural network,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation d6006c71-9d1f-47af-b37f-b491f1cf3408 · outbound

This paper cites A hybrid ann- snn architecture for low-power and low-latency visual perception,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A hybrid ann- snn architecture for low-power and low-latency visual perception,

Reference 27

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Observation ce11a76c-82e7-42a7-ad7e-ecb35b159553 · outbound

This paper cites Spiking neural networks on fpga: A survey of methodologies and recent advancements,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Spiking neural networks on fpga: A survey of methodologies and recent advancements,

Reference 28

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Observation 40954ba3-3c7c-437a-b076-aa765e4d7171 · outbound

This paper cites The intel neuromorphic dns challenge,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement The intel neuromorphic dns challenge,

Reference 29

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Observation 7cec81a8-6767-4b22-ac76-432a32b8639e · outbound

This paper cites A hybrid ann- snn architecture for low-power and low-latency visual perception,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A hybrid ann- snn architecture for low-power and low-latency visual perception,

Reference 30

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Observation e9847b36-0592-430c-9477-1dc29ea47be3 · outbound

This paper cites Hynita: A neuromorphic inference and training accelerator for hybrid ann-snn fusion models,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Hynita: A neuromorphic inference and training accelerator for hybrid ann-snn fusion models,

Reference 31

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Observation 9130e35c-0610-4781-8c6d-ddc26cc3cf1d · outbound

This paper cites Minimizing informa- tion loss reduces spiking neuronal networks to differential equations,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Minimizing informa- tion loss reduces spiking neuronal networks to differential equations,

Reference 32

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Observation 980a60c8-bdb2-45c1-bed8-e704d69539f8 · outbound

This paper cites Ai flow at the network edge,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Ai flow at the network edge,

Reference 33

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Observation dea79e77-07bb-4a4a-98f1-0c9e90bda308 · outbound

This paper cites Naturalspeech: End-to-end text-to-speech synthesis with human-level quality,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Naturalspeech: End-to-end text-to-speech synthesis with human-level quality,

Reference 34

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Observation 0bfbeeef-bea8-4266-87cb-a94e28ad5ad6 · outbound

This paper cites BoSS: Beyond-Semantic Speech.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement BoSS: Beyond-Semantic Speech

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:37:06.111137Z

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Observation d164540e-ee42-40a3-8df4-a4bbb0b45945 · outbound

This paper cites Fullsubnet+: Channel attention fullsubnet with complex spectrograms for speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Fullsubnet+: Channel attention fullsubnet with complex spectrograms for speech enhancement,

Reference 36

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Observation 87bb0623-a94f-4a7e-9ea9-2d2f66810dac · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Taylor, can you hear me now? a taylor-unfolding framework for monaural speech enhancement,

Reference 37

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Observation c38faa61-68ff-4381-a386-0f1c521e0e58 · outbound

This paper cites Comp- net: Complementary network for single-channel speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Comp- net: Complementary network for single-channel speech enhancement,

Reference 38

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Observation 818b9622-2738-405c-99e0-fc4ff462f265 · outbound

This paper cites Learning a spiking neural network for efficient image deraining,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Learning a spiking neural network for efficient image deraining,

Reference 39

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Observation 29a38930-b783-47cc-b22d-d78382adf665 · outbound

This paper cites Adaptation and learning of spatio-temporal thresholds in spiking neural networks,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Adaptation and learning of spatio-temporal thresholds in spiking neural networks,

Reference 40

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Observation d00f9c96-8fbf-4714-b7bd-013846c49a49 · outbound

This paper cites Enhancing representation of spiking neural networks via similarity- sensitive contrastive learning,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Enhancing representation of spiking neural networks via similarity- sensitive contrastive learning,

Reference 41

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Observation a009f631-620a-4107-94d2-b0806152280d · outbound

This paper cites Spikingbert: Distilling bert to train spiking language models using implicit differentiation,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Spikingbert: Distilling bert to train spiking language models using implicit differentiation,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 356e8e51-4b5d-4706-853f-28f41ec34001 · outbound

This paper cites Tc-lif: A two- compartment spiking neuron model for long-term sequential modelling,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Tc-lif: A two- compartment spiking neuron model for long-term sequential modelling,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation e500e48f-645f-4ea0-aae4-86c71071b37d · outbound

This paper cites Learning a spiking neural network for efficient image deraining,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Learning a spiking neural network for efficient image deraining,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation dc07aed1-6b27-4622-9bbb-91de66462da7 · outbound

This paper cites Spikelm: Towards general spike-driven language modeling via elastic bi-spiking mechanisms,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Spikelm: Towards general spike-driven language modeling via elastic bi-spiking mechanisms,

Reference 45

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Observation 0089ce88-a8ce-4694-8307-119d35b794fb · outbound

This paper cites Dpsnn: Spiking neural network for low- latency streaming speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dpsnn: Spiking neural network for low- latency streaming speech enhancement,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 24e7c898-0edb-409b-804d-b1a638c553f9 · outbound

This paper cites Temporally dynamic spiking transformer network for speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Temporally dynamic spiking transformer network for speech enhancement,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 4f801a00-ddbb-4190-b4f8-b635c88052dc · outbound

This paper cites Single channel speech enhancement using u-net spiking neural networks,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Single channel speech enhancement using u-net spiking neural networks,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation aa76be42-9f7f-4df0-8f19-95279c39207d · outbound

This paper cites Gerstner and W.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Gerstner and W

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation f87d911b-1a11-466f-8d7e-79cb3e4eff1b · outbound

This paper cites Rmp-loss: Regularizing membrane potential distribution for spiking neural networks,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Rmp-loss: Regularizing membrane potential distribution for spiking neural networks,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 441486f7-bdad-4c4c-aafe-5f8d807a2ad1 · outbound

This paper cites The design for the wall street journal-based csr corpus,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement The design for the wall street journal-based csr corpus,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation e5396445-9817-4f5f-83f9-2e0f31eeaac0 · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement The interspeech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results,

Reference 52

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Observation 5e195b44-08eb-416a-96a0-4f0c2761c0f4 · outbound

This paper cites Assessment for automatic speech recog- nition: Ii. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Assessment for automatic speech recog- nition: Ii. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems,

Reference 53

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Observation 0583c682-0c80-4735-8705-317a45fcaea5 · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 8070a8eb-3a49-4bb3-a568-f6cb6eb44639 · outbound

This paper cites Investigating rnn-based speech enhancement methods for noise-robust text-to-speech,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Investigating rnn-based speech enhancement methods for noise-robust text-to-speech,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation b649d9a7-f917-4b33-aa1d-a82f51891977 · outbound

This paper cites On the importance of power compression and phase estimation in monaural speech dereverberation,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement On the importance of power compression and phase estimation in monaural speech dereverberation,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation b42f9f18-7454-4d52-92bc-c413c2f3da24 · outbound

This paper cites Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 5549e1fd-c473-4cd0-88d8-a1d80993a241 · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Segan: Speech enhancement generative adversarial network,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 41caa21c-be19-47cc-bb54-5f56702a7802 · outbound

This paper cites Time-frequency masking-based speech enhancement using generative adversarial network,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Time-frequency masking-based speech enhancement using generative adversarial network,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 5f7bd2da-295e-47bb-b934-ca0371b3c0bb · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Metricgan: Generative adversarial networks based black-box metric scores optimization for speech enhancement,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 7c1da7bf-af2b-4878-8b46-4bb0212a6e74 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement WaveNet: A Generative Model for Raw Audio

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-07-02T15:37:06.105506Z

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Observation fb5eeaf6-c0bb-4a87-98b6-67fd6df1d973 · outbound

This paper cites Srtnet: Time domain speech enhancement via stochastic refinement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Srtnet: Time domain speech enhancement via stochastic refinement,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation c76b881e-6196-4f10-8224-53b3f19dcccc · outbound

This paper cites Phasen: A phase-and- harmonics-aware speech enhancement network,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Phasen: A phase-and- harmonics-aware speech enhancement network,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 4e726dcc-9178-4821-a44c-86650ee6968e · outbound

This paper cites Speech enhancement using self-adaptation and multi-head self- attention,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Speech enhancement using self-adaptation and multi-head self- attention,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 622c42ad-37d2-4d49-9155-254f4fdb4977 · outbound

This paper cites Tstnn: Two-stage transformer based neural network for speech enhancement in the time domain,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Tstnn: Two-stage transformer based neural network for speech enhancement in the time domain,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 581301c0-241f-42ab-b64a-86515171514e · outbound

This paper cites A multi-dimensional deep structured state space approach to speech enhancement using small- footprint models,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A multi-dimensional deep structured state space approach to speech enhancement using small- footprint models,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 9be1ffcb-fa57-4522-a358-3e48f0fa7cca · outbound

This paper cites A two-stage framework in cross-spectrum domain for real-time speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A two-stage framework in cross-spectrum domain for real-time speech enhancement,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 6f9f9755-9e33-4e62-a5f9-bb973960bad2 · outbound

This paper cites Dual-signal transformation lstm network for real-time noise suppression,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dual-signal transformation lstm network for real-time noise suppression,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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Observation 6e67fa91-34a2-4f11-b333-ff3f879fd63a · outbound

This paper cites Iifc-net: A monaural speech enhancement network with high-order information interaction and fea- ture calibration,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Iifc-net: A monaural speech enhancement network with high-order information interaction and fea- ture calibration,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation 6d869753-4b41-432c-9bb3-b4d149151603 · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement A mask free neural network for monaural speech enhancement,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation accfa4a0-d5e5-4949-834d-c434ecb6ac24 · outbound

This paper cites Sicrn: Advancing speech enhancement through state space model and inplace convolution techniques,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Sicrn: Advancing speech enhancement through state space model and inplace convolution techniques,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation e7ffd3cc-bf51-486b-9163-7a58fb06716a · outbound

This paper cites Exploiting bispectral features for single-channel speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Exploiting bispectral features for single-channel speech enhancement,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation c07df0b2-1fa6-44cb-b251-dbcae51eae44 · outbound

This paper cites Tsdt-net: Ultra- low-complexity two-stage model combining dual-path-transformer and transform-average-concatenate network for speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Tsdt-net: Ultra- low-complexity two-stage model combining dual-path-transformer and transform-average-concatenate network for speech enhancement,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation ca4f4f64-17da-48cc-9942-74411d617796 · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Per- ceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3cbeca0-06d4-4069-a4b1-f7eba7f232f6 · outbound

This paper cites An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation 1f90533c-00dc-42ad-8d80-3784326b271b · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Evaluation of objective quality measures for speech enhancement,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation b1f960ef-d0b6-4a5a-aef2-6f3d73cb5231 · outbound

This paper cites Dccrn+: Channel-wise subband dccrn with snr estimation for speech enhancement,.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Dccrn+: Channel-wise subband dccrn with snr estimation for speech enhancement,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

Source-reported events for the cited work

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Observation 33f5702c-582e-4f25-9843-c1df2806d55b · outbound

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

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement Densely connected neural network with dilated convolutions for real-time speech enhancement in the time domain,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-27T23:49:05.871422Z

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