Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation | Paper Citation Record

Observation 995f6bf7-549a-451b-8dcf-2432961da79a · outbound

This paper cites Yu and L.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Yu and L

Reference 1

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This paper cites Ro- bust stochastic approximation approach to stochastic pro- gramming,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Ro- bust stochastic approximation approach to stochastic pro- gramming,

Reference 2

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This paper cites Adam: A Method for Stochastic Optimization.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Adam: A Method for Stochastic Optimization

Reference 3

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This paper cites Comparison of parametric rep- resentations for monosyllabic word recognition in continu- ously spoken sentences,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Comparison of parametric rep- resentations for monosyllabic word recognition in continu- ously spoken sentences,

Reference 4

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This paper cites Efficient backprop,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Efficient backprop,

Reference 5

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This paper cites Rectified linear units improve restricted boltzmann machines,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Rectified linear units improve restricted boltzmann machines,

Reference 6

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This paper cites Rectifier non- linearities improve neural network acoustic models,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Rectifier non- linearities improve neural network acoustic models,

Reference 7

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This paper cites Searching for Activation Functions.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Searching for Activation Functions

Reference 8

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Observation bcd73ad9-a7dc-42e5-8a36-64c645c91e71 · outbound

This paper cites Gradient- based learning applied to document recognition,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Gradient- based learning applied to document recognition,

Reference 9

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This paper cites Long short-term mem- ory,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Long short-term mem- ory,

Reference 10

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Observation 90f3bfee-6505-402a-be93-2b00ebe74b2d · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 11

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Observation 38f50b0e-5c57-470a-9742-edb84c2b26fd · outbound

This paper cites Deep residual learning for image recognition,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Deep residual learning for image recognition,

Reference 12

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Observation c52a0ab4-6d25-4ae9-9080-61152700d3c4 · outbound

This paper cites Auto-Encoding Variational Bayes.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Auto-Encoding Variational Bayes

Reference 13

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This paper cites U-net: Convo- lutional networks for biomedical image segmentation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation U-net: Convo- lutional networks for biomedical image segmentation,

Reference 14

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 15

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This paper cites Layer Normalization.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Layer Normalization

Reference 16

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This paper cites Dropout: a simple way to prevent neural networks from overfitting,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Dropout: a simple way to prevent neural networks from overfitting,

Reference 17

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This paper cites Imagenet classification with deep convolutional neural networks,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Imagenet classification with deep convolutional neural networks,

Reference 18

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This paper cites Attention is all you need,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Attention is all you need,

Reference 19

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This paper cites Conformer: Convolution-augmented Transformer for Speech Recognition.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 20

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This paper cites V-net: Fully con- volutional neural networks for volumetric medical image seg- mentation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation V-net: Fully con- volutional neural networks for volumetric medical image seg- mentation,

Reference 21

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This paper cites A comparison of five multi- ple instance learning pooling functions for sound event de- tection with weak labeling,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation A comparison of five multi- ple instance learning pooling functions for sound event de- tection with weak labeling,

Reference 22

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Observation 34216c5e-80ad-4c87-a10b-8deced0a1c9f · outbound

This paper cites Support-vector networks,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Support-vector networks,

Reference 23

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This paper cites Training a support vector machine in the pri- mal,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Training a support vector machine in the pri- mal,

Reference 24

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This paper cites Robust truncated hinge loss support vector machines,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Robust truncated hinge loss support vector machines,

Reference 25

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This paper cites Robust estimation of a location parameter,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Robust estimation of a location parameter,

Reference 26

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This paper cites Ro- bust extended multidelay filter and double-talk detector for acoustic echo cancellation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Ro- bust extended multidelay filter and double-talk detector for acoustic echo cancellation,

Reference 27

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This paper cites The use of multiple measurements in taxo- nomic problems,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation The use of multiple measurements in taxo- nomic problems,

Reference 28

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This paper cites Fully-convolutional siamese networks for object tracking,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Fully-convolutional siamese networks for object tracking,

Reference 29

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This paper cites Facenet: A uni- fied embedding for face recognition and clustering,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Facenet: A uni- fied embedding for face recognition and clustering,

Reference 30

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This paper cites A simple framework for contrastive learning of visual representations,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation A simple framework for contrastive learning of visual representations,

Reference 31

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This paper cites Momentum contrast for unsupervised visual representation learning,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Momentum contrast for unsupervised visual representation learning,

Reference 32

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Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Self- supervised text-independent speaker verification using pro- totypical momentum contrastive learning,

Reference 33

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Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Wasserstein gen- erative adversarial networks,

Reference 34

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Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Improved training of wasserstein GANs,

Reference 35

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This paper cites Performance study of the MVDR beamformer as a function of the source incidence an- gle,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Performance study of the MVDR beamformer as a function of the source incidence an- gle,

Reference 36

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This paper cites ACCDOA: Activity-coupled cartesian direc- tion of arrival representation for sound event localization and detection,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation ACCDOA: Activity-coupled cartesian direc- tion of arrival representation for sound event localization and detection,

Reference 37

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This paper cites A deep learning framework for robust DOA estimation using spherical har- monic decomposition,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation A deep learning framework for robust DOA estimation using spherical har- monic decomposition,

Reference 38

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Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Ro- bust source counting and doa estimation using spatial pseudo- spectrum and convolutional neural network,

Reference 39

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This paper cites Multi-microphone speaker separation based on deep DOA estimation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Multi-microphone speaker separation based on deep DOA estimation,

Reference 40

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Observation cbd6ffef-7fd5-4a64-a464-6f1ed5f56844 · outbound

This paper cites Towards duration robust weakly supervised sound event detection,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Towards duration robust weakly supervised sound event detection,

Reference 41

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Observation 8edee82b-131e-4b84-b64b-287149c74465 · outbound

This paper cites Impact of sound duration and inactive frames on sound event detection performance,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Impact of sound duration and inactive frames on sound event detection performance,

Reference 42

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This paper cites Sound event detection and time-frequency segmentation from weakly labelled data,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Sound event detection and time-frequency segmentation from weakly labelled data,

Reference 43

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Observation 5f7e419e-b0da-456d-9bc6-a13791d107ac · outbound

This paper cites Weakly-supervised sound event detection with self-attention,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Weakly-supervised sound event detection with self-attention,

Reference 44

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Observation efc0226d-13cc-4e21-bd43-4c415171c58b · outbound

This paper cites Sound event detection by multitask learning of sound events and scenes with soft scene labels,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Sound event detection by multitask learning of sound events and scenes with soft scene labels,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:42.086125Z

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Observation 4e078444-aced-42f3-8bef-8e370449d397 · outbound

This paper cites Speaker recognition based on deep learning: An overview,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Speaker recognition based on deep learning: An overview,

Reference 46

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Observation cf06f2e9-535a-43d7-9b85-de050e06bd00 · outbound

This paper cites Front-end factor analysis for speaker verification,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Front-end factor analysis for speaker verification,

Reference 47

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Observation e1c0ea0b-abca-4d33-b69b-27944cdffcbd · outbound

This paper cites X-vectors: Robust DNN embeddings for speaker recognition,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation X-vectors: Robust DNN embeddings for speaker recognition,

Reference 48

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Observation ae92da9e-6efa-4c75-b47a-764614557d1c · outbound

This paper cites Data augmentation using deep generative models for embedding based speaker recognition,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Data augmentation using deep generative models for embedding based speaker recognition,

Reference 49

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Observation 1526eed1-1b63-40f9-b75b-102da6183557 · outbound

This paper cites Target speaker verification with selective auditory attention for single and multi-talker speech,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Target speaker verification with selective auditory attention for single and multi-talker speech,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:41.087788Z

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Observation cf10b5ed-99f2-4bec-a450-1071700a7c0f · outbound

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

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:40.912579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a7328e8-f697-46d3-b136-ca47fe4381a5 · outbound

This paper cites Pearson correlation coefficient,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Pearson correlation coefficient,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:40.793421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9f0140bc-c6ec-452a-83f8-55d3d7293dba · outbound

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

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Sdr– half-baked or well done?

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:40.602399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ce05622a-2ac7-4dd7-ab2f-edc6fd79f28f · outbound

This paper cites Learning spectral mapping for speech derever- beration and denoising,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Learning spectral mapping for speech derever- beration and denoising,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:40.455077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b5a9e9d5-a61d-426f-bdef-50dc3fd78626 · outbound

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

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation A convolutional recurrent neural net- work for real-time speech enhancement

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:40.279435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8a6fc2a-20ca-43f6-8608-2d667640e5eb · outbound

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

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Fullsubnet: A full- band and sub-band fusion model for real-time single-channel speech enhancement,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:40.013882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4ef0dcc7-fd53-4276-b2e7-89fe28988d8b · outbound

This paper cites SEGAN: Speech Enhancement Generative Adversarial Network.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation SEGAN: Speech Enhancement Generative Adversarial Network

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:33.998527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb96442-f7d5-4b09-b59e-f017115e2edd · outbound

This paper cites A new framework for CNN-based speech enhancement in the time domain,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation A new framework for CNN-based speech enhancement in the time domain,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:39.792793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 277a7ae1-61ff-4726-84bc-c37cbb9ff803 · outbound

This paper cites VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:34.109330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4f3793a-748e-4f71-ae24-d8dd6284364b · outbound

This paper cites Single channel target speaker extraction and recognition with speaker beam,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Single channel target speaker extraction and recognition with speaker beam,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:39.529064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 331633aa-604b-4951-ae54-1c806d41e81f · outbound

This paper cites Per- mutation invariant training of deep models for speaker- independent multi-talker speech separation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Per- mutation invariant training of deep models for speaker- independent multi-talker speech separation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:39.301821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 67e1e16a-0aa0-471f-82f1-708b11b74efe · outbound

This paper cites Multi-channel overlapped speech recognition with location guided speech extraction network,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Multi-channel overlapped speech recognition with location guided speech extraction network,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:39.054042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 34c3199e-f32c-4806-946d-1b3ec4939e02 · outbound

This paper cites Deep clustering: Discriminative embeddings for segmentation and separation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Deep clustering: Discriminative embeddings for segmentation and separation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:38.869359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef5876e5-bc7d-4a4a-b89e-a198efcd141d · outbound

This paper cites Combining spectral and spatial features for deep learning based blind speaker separation,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Combining spectral and spatial features for deep learning based blind speaker separation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:38.668927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6330c398-84cb-4a6f-9c94-fa3655abb6b7 · outbound

This paper cites Wavesplit: End-to-end speech separation by speaker clustering,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Wavesplit: End-to-end speech separation by speaker clustering,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:38.430092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation da9e39ab-0e33-4e61-8a5f-e51de4dc62df · outbound

This paper cites Real-time target sound extraction,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Real-time target sound extraction,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:38.253361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1b89cade-70ee-420f-a3d7-7ab3b1b5db6e · outbound

This paper cites Generative adversarial nets,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Generative adversarial nets,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:38.068855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 053a9073-b6b2-40d0-be56-f4e887b5af2b · outbound

This paper cites Conditional Generative Adversarial Nets.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Conditional Generative Adversarial Nets

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:34.841544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18cfadc5-b198-49ea-b0d3-25155c2e2f6b · outbound

This paper cites Unpaired image- to-image translation using cycle-consistent adversarial net- works,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Unpaired image- to-image translation using cycle-consistent adversarial net- works,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:37.861338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f9b44531-25f7-4d8b-8b53-949c5682495e · outbound

This paper cites Semi-Supervised Learning with Generative Adversarial Networks.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Semi-Supervised Learning with Generative Adversarial Networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:34.949510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9321beb-c316-4e30-92cd-c1b709328269 · outbound

This paper cites Villani et al., Optimal transport: old and new.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Villani et al., Optimal transport: old and new

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:37.676721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8bc9242f-e169-4fcf-b8ce-e184447ba540 · outbound

This paper cites A geometric view of optimal transportation and generative model,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation A geometric view of optimal transportation and generative model,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:37.508104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 36d41810-bb0b-4796-8b47-6236fe5f0286 · outbound

This paper cites The earth mover’s distance as a metric for image retrieval,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation The earth mover’s distance as a metric for image retrieval,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:37.350553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5f46bd4f-67f7-4020-923f-41f413a832f8 · outbound

This paper cites Zhou, Machine learning.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Zhou, Machine learning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:37.188946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71e48d70-e87b-441e-ac9f-1677fcace3bf · outbound

This paper cites Signal detection theory: Valuable tools for evaluating inductive learning,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Signal detection theory: Valuable tools for evaluating inductive learning,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:37.038973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 56ee3568-2c5d-46ed-a718-49a9a3110673 · outbound

This paper cites Understanding Diffusion Models: A Unified Perspective.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Understanding Diffusion Models: A Unified Perspective

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:35.519395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64d8a184-fb2c-4f7a-a298-4f9931092f39 · outbound

This paper cites Multidimensional scaling: I. Theory and method,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Multidimensional scaling: I. Theory and method,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:36.860877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation adb281cc-a0f2-4ae7-99ea-8c27f45691fe · outbound

This paper cites Stochastic neighbor embed- ding,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Stochastic neighbor embed- ding,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:36.681245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fe5d8293-84ad-4ca0-8d2f-20ad029862c0 · outbound

This paper cites Visualizing data using t- SNE,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Visualizing data using t- SNE,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:36.470139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cae69123-7b43-414e-b96b-fccd7df43af5 · outbound

This paper cites Nonlinear dimensionality re- duction by locally linear embedding,.

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation Nonlinear dimensionality re- duction by locally linear embedding,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:24:36.256905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Reference resolution

Outbound references

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