Paper Citation Record · LEDGER

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation

As of 5 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2509.09931.

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

pith.paper-citation-record.v1
2509.09931 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:29:06.368187Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 154bb9aa-8430-478f-b3c8-1d0c2eb8602e · outbound

This paper cites an unresolved cited work.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Unresolved cited work

Reference 1

Resolution: malformed identifier; no resolver link, observed 2026-08-04T18:29:06.322763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17965076-4fc0-4c6b-8ee4-bd64fd7a3514 · outbound

This paper cites Preprocessing The TAU urban acoustic scene 2022 mobile dataset contains recordings of 10 acoustic scenes in 12 European cities.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Preprocessing The TAU urban acoustic scene 2022 mobile dataset contains recordings of 10 acoustic scenes in 12 European cities

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.328528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e386ae28-23f1-4a58-87fc-c99af9994c4f · outbound

This paper cites The window length, hop length, FFT size , and number of mel bins are 8192, 1364, 8192, and 256, respectively.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation The window length, hop length, FFT size , and number of mel bins are 8192, 1364, 8192, and 256, respectively

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.332717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5d04ed4-7ebc-465b-8d7e-6e4c1849c758 · outbound

This paper cites The proposed model is based on CNN and is trained solely on the TAU Urban Acoustic Scene 2022 Mobile development dataset.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation The proposed model is based on CNN and is trained solely on the TAU Urban Acoustic Scene 2022 Mobile development dataset

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.336828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ebefc6f-f181-4646-bd93-ff42a06e0d90 · outbound

This paper cites an unresolved cited work.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.341418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02d76098-344f-4e27-b085-a526808037e5 · outbound

This paper cites Data-efficient l ow-complexity acoustic sce- ne classification in the DCASE 2024 challenge,.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Data-efficient l ow-complexity acoustic sce- ne classification in the DCASE 2024 challenge,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.345913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e392a58-f350-429c-b17b-7ef59e79b0be · outbound

This paper cites Heittola, A.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Heittola, A

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.349713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c630a4ee-01e0-4275-9eca-176eaf26c724 · outbound

This paper cites an unresolved cited work.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.353503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d1095e-b2bd-4543-b8dd-64d664d2a543 · outbound

This paper cites librosa: Audio and music signal analysis in python,.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation librosa: Audio and music signal analysis in python,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.357198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 916969ab-7a6e-424a-868d-6f239bc64cf4 · outbound

This paper cites SpecAugment: A simple data augmenta- tion method for automatic speech recognition,.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation SpecAugment: A simple data augmenta- tion method for automatic speech recognition,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.360989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed3306f5-c74a-4c4b-aad6-3fe03a602ed8 · outbound

This paper cites Distilling the knowledge of transformers and CNNs with CP-mobile. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2023 Work- shop (DCASE2023),.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation Distilling the knowledge of transformers and CNNs with CP-mobile. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2023 Work- shop (DCASE2023),

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.364953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9b01879-ae26-4d9d-9dfc-a69afcfcc617 · outbound

This paper cites De- vice robust acoustic scene classification via impulse re- sponse augmentation,.

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation De- vice robust acoustic scene classification via impulse re- sponse augmentation,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T18:29:06.368187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.