Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:36:53.842471Z
Paper Citation Record · LEDGER
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4
As of 10 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.21174.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
21 of 21 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 594845b1-4df0-45ac-9b2f-5fbbad5e9c55 · outbound
This paper cites This complex task requires a system to identify active sound classes (audio tagging) and to isolate their corresponding anechoic source signals accurately.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 This complex task requires a system to identify active sound classes (audio tagging) and to isolate their corresponding anechoic source signals accurately
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation 6d07eef9-ab98-4f10-b40b-044f7210a1ab · outbound
This paper cites This section outlines the com- position of the official dataset and the specific data curation and augmentation steps to improve the performance of the model.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 This section outlines the com- position of the official dataset and the specific data curation and augmentation steps to improve the performance of the model
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation 9aef203b-bec4-4fff-b5d5-01e5669690f0 · outbound
This paper cites an unresolved cited work.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Unresolved cited work
Reference 3
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Source-reported events for the cited work
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Observation df393d6e-d508-49e4-83be-ef658276eaea · outbound
This paper cites Model training The audio-tagging and source-separation models were trained in- dependently.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Model training The audio-tagging and source-separation models were trained in- dependently
Reference 4
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Source-reported events for the cited work
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Observation 7d65e086-be41-4beb-b043-31c4a2c1be09 · outbound
This paper cites The proposed strategy combined additional audio feature input (spectral roll-off and chroma), a dataset refinement process, and an agent-based er- ror correction system.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 The proposed strategy combined additional audio feature input (spectral roll-off and chroma), a dataset refinement process, and an agent-based er- ror correction system
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation b8cb2d5b-f9bb-406d-8a2e-200ac2f866fd · outbound
This paper cites SpatialScaper: A library to simulate and augment soundscapes for sound event localization and detection in re- alistic rooms,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 SpatialScaper: A library to simulate and augment soundscapes for sound event localization and detection in re- alistic rooms,
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation c64136c5-da74-48c4-865f-95c227379066 · outbound
This paper cites FSD50K: An open dataset of human -labeled sound events,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 FSD50K: An open dataset of human -labeled sound events,
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7fe2ea60-7d33-4e04-b7c3-bff7316d9dae · outbound
This paper cites Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 7020296b-b2a9-42cf-b4d9-ecbe937a319f · outbound
This paper cites Baseline Systems and Evaluation Metrics for Spatial Semantic Segmentation of Sound Scenes.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Baseline Systems and Evaluation Metrics for Spatial Semantic Segmentation of Sound Scenes
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a98befa5-111e-4ae3-83a4-b9bf9eb68ec6 · outbound
This paper cites an unresolved cited work.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Unresolved cited work
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 3cfc95c4-4be1-4441-81b5-2830196e37f8 · outbound
This paper cites All audio mixes of 10 seconds long each were generated at a sampling rate of 32 kHz.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 All audio mixes of 10 seconds long each were generated at a sampling rate of 32 kHz
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ea2b1f06-cf0c-40a7-9cb1-7769dbc84a58 · outbound
This paper cites Classification in the presence of label noise: A survey,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Classification in the presence of label noise: A survey,
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8f659957-e00e-4b53-9744-bb82fbce96c8 · outbound
This paper cites Acoustic classification and seg- mentation using modified spectral roll-off and variance-based features.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Acoustic classification and seg- mentation using modified spectral roll-off and variance-based features
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 9f57c45c-423a-40b3-9400-ff259b0e0f05 · outbound
This paper cites Making chorma features more robust to timber changes.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Making chorma features more robust to timber changes
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation 2b769b2d-e693-43e8-a747-fdbea6bcf71f · outbound
This paper cites EARS: An anechoic fullband speech dataset benchmarked for speech enhancement and dereverbera- tion,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 EARS: An anechoic fullband speech dataset benchmarked for speech enhancement and dereverbera- tion,
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8c429450-61fb-43f4-bc84-13cd888635b0 · outbound
This paper cites Echo-aware adaptation of sound event localization and detection in unknown envi- ronments,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Echo-aware adaptation of sound event localization and detection in unknown envi- ronments,
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation ad108375-86e0-4399-a657-6c546a9420cc · outbound
This paper cites ESC: Dataset for environmental sound classi- fication,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 ESC: Dataset for environmental sound classi- fication,
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 8be1995d-3557-4b11-9a06-cda8c31233ba · outbound
This paper cites 13, 2025).
Reference 18
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a93be642-2af9-4dd4-a2f8-3f88dbf1fad2 · outbound
This paper cites Audio set: An ontology and human- labeled dataset for audio events,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Audio set: An ontology and human- labeled dataset for audio events,
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8abe9694-6c7f-4345-a470-a39c478645ad · outbound
This paper cites Masked modeling duo: Towards a universal audio pre-training fram ework,.
Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4 Masked modeling duo: Towards a universal audio pre-training fram ework,
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5b41a0eb-11a9-4b2a-826a-8d0bb5f47fb4 · outbound
This paper cites 13, 2025).
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
No inbound Pith citation observations are available.