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Acoustic Scene Classification

The goal of acoustic scene classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded.

Source: DCASE 2019 Source: DCASE 2018

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 11–20 of 132 papers

Title	Date	Tasks	Status	Hype	Score
Receptive-field-regularized CNN variants for acoustic scene classification	Sep 5, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	1	5
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift	Feb 5, 2024	Acoustic Scene ClassificationScene Classification	CodeCode Available	1	5
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events	Sep 21, 2020	Acoustic Scene ClassificationAudio Tagging	CodeCode Available	1	5
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording Devices	May 25, 2021	Acoustic Scene ClassificationClassification	CodeCode Available	1	5
Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification	Oct 5, 2023	Acoustic Scene ClassificationGraph Representation Learning	CodeCode Available	1	5
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation	Jul 16, 2020	Acoustic Scene ClassificationData Augmentation	CodeCode Available	1	5
A Two-Stage Approach to Device-Robust Acoustic Scene Classification	Nov 3, 2020	Acoustic Scene ClassificationClassification	CodeCode Available	1	5
Efficient Training of Audio Transformers with Patchout	Oct 11, 2021	Acoustic Scene ClassificationAudio Classification	CodeCode Available	1	5
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge	May 16, 2024	Acoustic Scene ClassificationScene Classification	CodeCode Available	0	5
CochlScene: Acquisition of acoustic scene data using crowdsourcing	Nov 4, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available	0	5

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All datasets CochlScene DCASE 2019 Mobile TAU Urban Acoustic Scenes 2019 TUT Acoustic Scenes 2017 TUT Urban Acoustic Scenes 2018

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Audio Flamingo	1:1 Accuracy	0.83	—	Unverified
2	Qwen-Audio	1:1 Accuracy	0.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Basic + Spectrum Correction	Accuracy	70.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Two-stage ensemble system	1:1 Accuracy	81.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Qwen-Audio	1:1 Accuracy	0.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ERGL: event relational graph representation learning	Acc	78.1	—	Unverified