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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 31–40 of 132 papers

Title	Date	Tasks	Status	Hype
Incremental Learning of Acoustic Scenes and Sound Events	Feb 28, 2023	Acoustic Scene ClassificationAudio Tagging	—Unverified	0
Short-Term Memory Convolutions	Feb 8, 2023	Acoustic Scene ClassificationScene Classification	—Unverified	0
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms	Nov 17, 2022	Acoustic Scene ClassificationAnomaly Detection	CodeCode Available	0
CochlScene: Acquisition of acoustic scene data using crowdsourcing	Nov 4, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available	0
Efficient Similarity-based Passive Filter Pruning for Compressing CNNs	Oct 27, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available	0
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification	Oct 27, 2022	Acoustic Scene ClassificationGraph Representation Learning	CodeCode Available	1
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context	Oct 16, 2022	Acoustic Scene ClassificationScene Classification	—Unverified	0
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification	Sep 13, 2022	Acoustic Scene ClassificationEvent Detection	—Unverified	0
Low-complexity CNNs for Acoustic Scene Classification	Aug 2, 2022	Acoustic Scene ClassificationClassification	—Unverified	0
Low-complexity CNNs for Acoustic Scene Classification	Jul 23, 2022	Acoustic Scene ClassificationClassification	—Unverified	0

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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