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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

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Showing 21–30 of 132 papers

Title	Date	Tasks	Status	Hype	Score
Domain Information Control at Inference Time for Acoustic Scene Classification	Jun 13, 2023	Acoustic Scene ClassificationDomain Generalization	CodeCode Available	0	5
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification	Jun 19, 2018	Acoustic Scene ClassificationClassification	CodeCode Available	0	5
Efficient Similarity-based Passive Filter Pruning for Compressing CNNs	Oct 27, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available	0	5
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge	May 3, 2025	Acoustic Scene ClassificationScene Classification	CodeCode Available	0	5
Acoustic scene classification using auditory datasets	Dec 26, 2021	Acoustic Scene ClassificationClassification	CodeCode Available	0	5
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification	Mar 29, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available	0	5
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling	Jul 11, 2016	Acoustic Scene ClassificationAll	CodeCode Available	0	5
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	5
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification	Sep 4, 2019	Acoustic Scene ClassificationBIG-bench Machine Learning	CodeCode Available	0	5
City classification from multiple real-world sound scenes	Jul 29, 2019	Acoustic Scene ClassificationClassification	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