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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 21–30 of 132 papers

Title	Date	Tasks	Status	Hype
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification	Sep 28, 2023	Acoustic Scene ClassificationScene Classification	CodeCode Available	0
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers	Jun 20, 2023	Acoustic Scene ClassificationScene Classification	—Unverified	0
Domain Information Control at Inference Time for Acoustic Scene Classification	Jun 13, 2023	Acoustic Scene ClassificationDomain Generalization	CodeCode Available	0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration	Jun 9, 2023	Acoustic Scene ClassificationAudio Signal Processing	—Unverified	0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet	Jun 3, 2023	Acoustic Scene ClassificationData Augmentation	—Unverified	0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes	May 29, 2023	Acoustic Scene ClassificationContinual Learning	—Unverified	0
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms	May 16, 2023	Acoustic Scene ClassificationScene Classification	—Unverified	0
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation	May 12, 2023	Acoustic Scene ClassificationAudio Classification	CodeCode Available	1
Compressing audio CNNs with graph centrality based filter pruning	May 5, 2023	Acoustic Scene ClassificationAudio Classification	—Unverified	0
Unsupervised Improvement of Audio-Text Cross-Modal Representations	May 3, 2023	Acoustic Scene ClassificationClassification	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