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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 121–130 of 132 papers

Title	Date	Tasks	Status	Hype
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification	Mar 29, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available	0
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer	Oct 16, 2021	Acoustic Scene ClassificationScene Classification	CodeCode Available	0
A multi-device dataset for urban acoustic scene classification	Jul 25, 2018	Acoustic Scene ClassificationClassification	CodeCode Available	0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning	Nov 21, 2023	Acoustic Scene ClassificationAudio captioning	CodeCode Available	0
Unsupervised adversarial domain adaptation for acoustic scene classification	Aug 17, 2018	Acoustic Scene ClassificationClassification	CodeCode Available	0
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms	Nov 17, 2022	Acoustic Scene ClassificationAnomaly Detection	CodeCode Available	0
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events	Apr 10, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	0
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification	Apr 24, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	0
Acoustic scene analysis with multi-head attention networks	Sep 16, 2019	Acoustic Scene ClassificationGeneral Classification	CodeCode Available	0
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems	May 28, 2021	Acoustic Scene ClassificationQuantization	CodeCode Available	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