Paper Citation Record · LEDGER

Explainability of CNN Based Classification Models for Acoustic Signal

As of 22 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2509.08717.

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

pith.paper-citation-record.v1
2509.08717 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:09:15.723078Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T02:19:51.986991Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:21:16.635547Z

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy27
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e79e8a2-4dbf-4cfa-ac0e-9b734a876c88 · outbound

This paper cites Acoustic analysis of speech,.

Explainability of CNN Based Classification Models for Acoustic Signal Acoustic analysis of speech,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.776274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8ca8c0fc-8d96-45b7-bd09-d00b566ddbe8 · outbound

This paper cites Biosignal sensors and deep learning-based speech recognition: A review,.

Explainability of CNN Based Classification Models for Acoustic Signal Biosignal sensors and deep learning-based speech recognition: A review,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.670822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2e3d0a95-5133-4519-9256-813ec6914e44 · outbound

This paper cites Vibration feature extraction using signal processing techniques for structural health monitoring: A review,.

Explainability of CNN Based Classification Models for Acoustic Signal Vibration feature extraction using signal processing techniques for structural health monitoring: A review,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.618555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0e1e64e-2cb1-4b5f-a464-65f3d108da73 · outbound

This paper cites Generalisation in environ- mental sound classification: the ‘making sense of sounds’ data set and challenge,.

Explainability of CNN Based Classification Models for Acoustic Signal Generalisation in environ- mental sound classification: the ‘making sense of sounds’ data set and challenge,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.591287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b50ff985-0116-4ded-98b8-470f8a3da183 · outbound

This paper cites The function(s) of bird song,.

Explainability of CNN Based Classification Models for Acoustic Signal The function(s) of bird song,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.567959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b44414cc-fef4-4be4-9424-4a6a388d72aa · outbound

This paper cites A survey of audio classification using deep learning,.

Explainability of CNN Based Classification Models for Acoustic Signal A survey of audio classification using deep learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.547306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b7fa0aa4-f346-4899-9b23-54fc60cc25d7 · outbound

This paper cites Exploring ex- plainable ai methods for bird sound-based species recognition systems,.

Explainability of CNN Based Classification Models for Acoustic Signal Exploring ex- plainable ai methods for bird sound-based species recognition systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.515783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f87b6e29-4a4d-43f9-a7b7-82272752c101 · outbound

This paper cites Audio explainable artificial intelligence: A review,.

Explainability of CNN Based Classification Models for Acoustic Signal Audio explainable artificial intelligence: A review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.403598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e89eb7d9-841d-4bef-9c4e-ee6216d26ffb · outbound

This paper cites Song learning, dialects, and dispersal in the bewick’s wren,.

Explainability of CNN Based Classification Models for Acoustic Signal Song learning, dialects, and dispersal in the bewick’s wren,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.362601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 117280bc-393b-43d1-82ed-db1ad12bc3f4 · outbound

This paper cites Seewave, a free modular tool for sound analysis and synthesis,.

Explainability of CNN Based Classification Models for Acoustic Signal Seewave, a free modular tool for sound analysis and synthesis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.342793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5b75869-f850-4b72-a1b8-b5fd493e527c · outbound

This paper cites Why should i trust you? explaining the predictions of any classifier,.

Explainability of CNN Based Classification Models for Acoustic Signal Why should i trust you? explaining the predictions of any classifier,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.322713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cee5fce9-8758-4f82-ac73-fa425da17f40 · outbound

This paper cites A unified approach to interpreting model predictions,.

Explainability of CNN Based Classification Models for Acoustic Signal A unified approach to interpreting model predictions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.302892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b445143b-31a0-443d-aab0-beea8ff3a2a4 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Explainability of CNN Based Classification Models for Acoustic Signal Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.279465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 67bc76b3-1163-4fb9-8c86-76ce601e7568 · outbound

This paper cites Learning Important Features Through Propagating Activation Differences.

Explainability of CNN Based Classification Models for Acoustic Signal Learning Important Features Through Propagating Activation Differences

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.567326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c103832-059a-4b06-b684-9d9c035e3517 · outbound

This paper cites Visualizing data using t-sne.

Explainability of CNN Based Classification Models for Acoustic Signal Visualizing data using t-sne

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.575404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3b27950-f144-4b06-b3e7-9aa92a531de8 · outbound

This paper cites Principal component analysis,.

Explainability of CNN Based Classification Models for Acoustic Signal Principal component analysis,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.585390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65dd6c2d-9d6b-4743-845e-716b2f94311a · outbound

This paper cites Environmental sound classification with convolutional neural networks,.

Explainability of CNN Based Classification Models for Acoustic Signal Environmental sound classification with convolutional neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.227954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 70f3e5e6-f0ec-4078-8807-b62d5ee9cc85 · outbound

This paper cites Automatic acoustic detection of birds through deep learning: the first bird audio detection challenge,.

Explainability of CNN Based Classification Models for Acoustic Signal Automatic acoustic detection of birds through deep learning: the first bird audio detection challenge,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.201828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e6d1a36e-8188-482f-b02e-c52d6df115ab · outbound

This paper cites Birdnet: A deep learning solution for avian diversity monitoring,.

Explainability of CNN Based Classification Models for Acoustic Signal Birdnet: A deep learning solution for avian diversity monitoring,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.181050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ae33bd28-142b-454a-afcb-264ea2b9a050 · outbound

This paper cites A machine learning approach for classifying and quantifying acoustic diversity,.

Explainability of CNN Based Classification Models for Acoustic Signal A machine learning approach for classifying and quantifying acoustic diversity,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.149927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c8b4a9d1-6cea-497a-86f7-7c0f336f6e89 · outbound

This paper cites Deep convolutional neural networks and data augmentation for environmental sound classification,.

Explainability of CNN Based Classification Models for Acoustic Signal Deep convolutional neural networks and data augmentation for environmental sound classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.131226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee09ee8f-bc5e-4987-b44b-03dccd152135 · outbound

This paper cites Birdsong classification based on ensemble multi-scale convolutional neural network,.

Explainability of CNN Based Classification Models for Acoustic Signal Birdsong classification based on ensemble multi-scale convolutional neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.112267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fc5d87b4-a3eb-46a4-ad08-6988a6603112 · outbound

This paper cites Bird species recognition using support vector machines,.

Explainability of CNN Based Classification Models for Acoustic Signal Bird species recognition using support vector machines,

Reference 23

Resolution: verified exact; doi, observed 2026-08-15T16:09:15.781891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c04b44ac-b1b4-4d12-a9a8-148dcf6b37f7 · outbound

This paper cites Computational bioacoustics with deep learning: A review and roadmap,.

Explainability of CNN Based Classification Models for Acoustic Signal Computational bioacoustics with deep learning: A review and roadmap,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.634639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 382194f0-e58a-4354-a1a6-62ac664a5cb7 · outbound

This paper cites Explainable artificial intelligence (xai): Concepts, tax- onomies, opportunities and challenges toward responsible ai,.

Explainability of CNN Based Classification Models for Acoustic Signal Explainable artificial intelligence (xai): Concepts, tax- onomies, opportunities and challenges toward responsible ai,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.072802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b621d728-df25-4083-96d4-e0c6b9eed98c · outbound

This paper cites Axiomatic attribution for deep networks,.

Explainability of CNN Based Classification Models for Acoustic Signal Axiomatic attribution for deep networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.051115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3e64f39-adc6-4558-a78b-9b73e15521ef · outbound

This paper cites The Mythos of Model Interpretability.

Explainability of CNN Based Classification Models for Acoustic Signal The Mythos of Model Interpretability

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.654959Z

Source-reported events for the cited work

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Observation 883bb24b-8d3d-4d9a-9b53-5140823547b7 · outbound

This paper cites Jeyasothy, T.

Explainability of CNN Based Classification Models for Acoustic Signal Jeyasothy, T

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:16.027031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cdd84a26-be61-44cc-a97b-3c517df0ab41 · outbound

This paper cites Artificial intelligence in landscape ecology: Recent advances, perspectives, and opportunities,.

Explainability of CNN Based Classification Models for Acoustic Signal Artificial intelligence in landscape ecology: Recent advances, perspectives, and opportunities,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:15.995729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a2b1c5d8-854d-47c1-a315-5d4e6a15c69b · outbound

This paper cites Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models,.

Explainability of CNN Based Classification Models for Acoustic Signal Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:15.976122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3f036f71-f110-4be0-9d5c-49efa018d3dd · outbound

This paper cites Categorizing shallow marine soundscapes using explained clusters,.

Explainability of CNN Based Classification Models for Acoustic Signal Categorizing shallow marine soundscapes using explained clusters,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:15.958015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4d6756bc-49d9-4c13-acdd-797d7408853d · outbound

This paper cites Exploring explainable ai methods for bird sound-based species recognition systems,.

Explainability of CNN Based Classification Models for Acoustic Signal Exploring explainable ai methods for bird sound-based species recognition systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:15.937493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5b151264-2555-4df6-953f-1db0a99ddaae · outbound

This paper cites Bridging ai and ecology: Cilnn and xai for acoustic based prediction of dangerous wild animals,.

Explainability of CNN Based Classification Models for Acoustic Signal Bridging ai and ecology: Cilnn and xai for acoustic based prediction of dangerous wild animals,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:09:15.917249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d6c16ad2-9bde-451f-8d83-7fb0d3ca75a1 · outbound

This paper cites Distributionally Robust Receive Combining.

Explainability of CNN Based Classification Models for Acoustic Signal Distributionally Robust Receive Combining

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.723078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f7382fd-8876-4f84-a6a6-392a1ded6492 · outbound

This paper cites animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics.

Explainability of CNN Based Classification Models for Acoustic Signal animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:15.707584Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8c127e91-e340-418c-af01-649def111492 · inbound

Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging cites this paper.

Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging Explainability of CNN Based Classification Models for Acoustic Signal

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.637465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.