Paper Citation Record · LEDGER

Receptive-field-regularized CNN variants for acoustic scene classification

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:1909.02859.

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

pith.paper-citation-record.v1
1909.02859 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:55:46.403535Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:55:46.273289Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:06:24.452549Z

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy23
unresolved7
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b17a9db-99fd-499d-a493-4f8c3d52955d · outbound

This paper cites an unresolved cited work.

Receptive-field-regularized CNN variants for acoustic scene classification Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:55:46.899090Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28a0a3ee-369c-4354-a6f8-48f2597aa596 · outbound

This paper cites Receptive-field-regularized CNN variants for acoustic scene classification.

Receptive-field-regularized CNN variants for acoustic scene classification Receptive-field-regularized CNN variants for acoustic scene classification

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T04:55:46.273289Z

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Observation c0b7888b-171e-41bd-8a5b-ed97e64ab0ef · outbound

This paper cites Data Preparation and Training We extracted the input features using a Short Time Fourier Trans- form (STFT) with a window size of 2048 and 25% overlap.

Receptive-field-regularized CNN variants for acoustic scene classification Data Preparation and Training We extracted the input features using a Short Time Fourier Trans- form (STFT) with a window size of 2048 and 25% overlap

Reference 3

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Observation ab17842b-7e60-41af-9a7b-a2c980fe10aa · outbound

This paper cites It is also worth noting that the maximum RF is different from the effective RF as explained in [1, 25].

Receptive-field-regularized CNN variants for acoustic scene classification It is also worth noting that the maximum RF is different from the effective RF as explained in [1, 25]

Reference 4

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Observation 77d4c968-3030-4c26-beaa-ba93ffe5238b · outbound

This paper cites an unresolved cited work.

Receptive-field-regularized CNN variants for acoustic scene classification Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:55:46.850894Z

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Observation 8d4f3d13-95fd-4be4-b57c-e805ee1da440 · outbound

This paper cites Detection and Classiﬁcation of Acoustic Scenes and Events 2019 25–26 October 2019, New York, NY , USA.

Receptive-field-regularized CNN variants for acoustic scene classification Detection and Classiﬁcation of Acoustic Scenes and Events 2019 25–26 October 2019, New York, NY , USA

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.834547Z

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Observation 9493499b-84f4-45f0-aad0-62e60447543f · outbound

This paper cites The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classiﬁcation,.

Receptive-field-regularized CNN variants for acoustic scene classification The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classiﬁcation,

Reference 7

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Observation 43bca154-2e7e-48c9-b1b5-c319e10448e4 · outbound

This paper cites CP- JKU submissions for DCASE-2016: A hybrid approach using binaural i-vectors and deep convolutional neural networks,.

Receptive-field-regularized CNN variants for acoustic scene classification CP- JKU submissions for DCASE-2016: A hybrid approach using binaural i-vectors and deep convolutional neural networks,

Reference 8

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Observation 338ee41c-6f4c-4d59-8de4-82ce5e0d8ba5 · outbound

This paper cites CNN ar- chitectures for large-scale audio classiﬁcation,.

Receptive-field-regularized CNN variants for acoustic scene classification CNN ar- chitectures for large-scale audio classiﬁcation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.787782Z

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Observation 201a7a5b-b3db-4512-a654-be885a587740 · outbound

This paper cites Classifying short acoustic scenes with I-vectors and CNNs: Challenges and optimisations for the 2017 DCASE ASC task,.

Receptive-field-regularized CNN variants for acoustic scene classification Classifying short acoustic scenes with I-vectors and CNNs: Challenges and optimisations for the 2017 DCASE ASC task,

Reference 10

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Observation aaba4f49-7677-4133-85fc-c9232d3fadf0 · outbound

This paper cites Acoustic scene classiﬁcation with fully convolutional neural networks and I-vectors,.

Receptive-field-regularized CNN variants for acoustic scene classification Acoustic scene classiﬁcation with fully convolutional neural networks and I-vectors,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.759338Z

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Observation 11bf2369-bfe3-4bcf-a932-e889c5a0495d · outbound

This paper cites Acoustic scene classiﬁcation by ensemble of spectrograms based on adaptive temporal divi- sions.

Receptive-field-regularized CNN variants for acoustic scene classification Acoustic scene classiﬁcation by ensemble of spectrograms based on adaptive temporal divi- sions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.745697Z

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Observation 37b856c6-c0b4-489b-b61b-48df1f133c10 · outbound

This paper cites Training general-purpose au- dio tagging networks with noisy labels and iterative self- veriﬁcation,.

Receptive-field-regularized CNN variants for acoustic scene classification Training general-purpose au- dio tagging networks with noisy labels and iterative self- veriﬁcation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.731167Z

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Observation bbe94790-847a-4fdb-a47b-557c7b1c49cb · outbound

This paper cites Stacked convolutional neural networks for general-purpose audio tag- ging.

Receptive-field-regularized CNN variants for acoustic scene classification Stacked convolutional neural networks for general-purpose audio tag- ging

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.715632Z

Source-reported events for the cited work

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Observation 2b640490-4cb9-434c-b9e5-f68c26e6b215 · outbound

This paper cites Ensemble of convolu- tional neural networks for weakly-supervised sound event de- tection using multiple scale input.

Receptive-field-regularized CNN variants for acoustic scene classification Ensemble of convolu- tional neural networks for weakly-supervised sound event de- tection using multiple scale input

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.701581Z

Source-reported events for the cited work

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Observation ca36b18b-6c5b-495d-a73b-513339b1e2e7 · outbound

This paper cites Iterative knowledge distillation in R-CNNs for weakly-labeled semi- supervised sound event detection,.

Receptive-field-regularized CNN variants for acoustic scene classification Iterative knowledge distillation in R-CNNs for weakly-labeled semi- supervised sound event detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.687307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3f17040a-62e9-4d9a-9ba5-779877d151c9 · outbound

This paper cites Deep residual learning for image recognition,.

Receptive-field-regularized CNN variants for acoustic scene classification Deep residual learning for image recognition,

Reference 17

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Source-reported events for the cited work

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Observation 2fc9ed72-3329-4fb7-bf63-0c0ccbf422f9 · outbound

This paper cites Densely connected convolutional networks,.

Receptive-field-regularized CNN variants for acoustic scene classification Densely connected convolutional networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.657695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7de3eb81-22bf-40ba-b279-fa4f58284a23 · outbound

This paper cites Acoustic scene classiﬁcation and audio tagging with receptive-ﬁeld- regularized CNNs,.

Receptive-field-regularized CNN variants for acoustic scene classification Acoustic scene classiﬁcation and audio tagging with receptive-ﬁeld- regularized CNNs,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.643348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e846d2e8-305e-4ea0-accd-5c4dc307d9f9 · outbound

This paper cites Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet,.

Receptive-field-regularized CNN variants for acoustic scene classification Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.629035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 38076762-066e-4984-8f2d-8c6b33f5a127 · outbound

This paper cites Dynamic routing be- tween capsules,.

Receptive-field-regularized CNN variants for acoustic scene classification Dynamic routing be- tween capsules,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.614082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81be1d10-1460-43bc-b73e-bd0ea77e4afd · outbound

This paper cites An intriguing failing of convolutional neural networks and the coordconv solution,.

Receptive-field-regularized CNN variants for acoustic scene classification An intriguing failing of convolutional neural networks and the coordconv solution,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.598775Z

Source-reported events for the cited work

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Observation ed112459-a560-4f28-a84c-d73ff951990e · outbound

This paper cites Identity Mappings in Deep Residual Networks.

Receptive-field-regularized CNN variants for acoustic scene classification Identity Mappings in Deep Residual Networks

Reference 23

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Source-reported events for the cited work

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Observation f350432e-d047-4bda-865f-284be69b6faf · outbound

This paper cites Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition.

Receptive-field-regularized CNN variants for acoustic scene classification Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition

Reference 24

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Observation ed96f213-20c1-4286-a6f3-31da32ca0dcc · outbound

This paper cites Shake-Shake regularization.

Receptive-field-regularized CNN variants for acoustic scene classification Shake-Shake regularization

Reference 25

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Source-reported events for the cited work

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Observation c28de80e-0889-428a-88ba-92848eff9085 · outbound

This paper cites Acoustic scene clas- siﬁcation in DCASE 2019 challenge: Closed and open set classiﬁcation and data mismatch setups,.

Receptive-field-regularized CNN variants for acoustic scene classification Acoustic scene clas- siﬁcation in DCASE 2019 challenge: Closed and open set classiﬁcation and data mismatch setups,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.582572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7156d64a-f3dc-44d4-b7f9-8644176737af · outbound

This paper cites an unresolved cited work.

Receptive-field-regularized CNN variants for acoustic scene classification Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:55:46.567256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 009d2cd6-8218-4fab-8f8a-c2538af8e7b9 · outbound

This paper cites Audio tagging with noisy labels and minimal supervision.

Receptive-field-regularized CNN variants for acoustic scene classification Audio tagging with noisy labels and minimal supervision

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T04:55:46.390164Z

Source-reported events for the cited work

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Observation 5b5028b5-2c9d-44f7-b930-861903eb897b · outbound

This paper cites Adam: A method for stochastic optimization,.

Receptive-field-regularized CNN variants for acoustic scene classification Adam: A method for stochastic optimization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.552509Z

Source-reported events for the cited work

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Observation 6de79495-8341-4f0e-8021-bb6019d73aa1 · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Receptive-field-regularized CNN variants for acoustic scene classification mixup: Beyond empirical risk minimization,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.536895Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05b7103d-c90b-403d-adb3-621184dba90e · outbound

This paper cites Understanding the Effective Receptive Field in Deep Convolutional Neural Net- works,.

Receptive-field-regularized CNN variants for acoustic scene classification Understanding the Effective Receptive Field in Deep Convolutional Neural Net- works,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:55:46.521661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 28a0a3ee-369c-4354-a6f8-48f2597aa596 · inbound