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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 61–70 of 132 papers

Title	Date	Tasks	Status	Hype
Efficient Training of Audio Transformers with Patchout	Oct 11, 2021	Acoustic Scene ClassificationAudio Classification	CodeCode Available	1
Visually Exploring Multi-Purpose Audio Data	Oct 9, 2021	Acoustic Scene ClassificationClassification	—Unverified	0
An evaluation of data augmentation methods for sound scene geotagging	Oct 9, 2021	Acoustic Scene ClassificationClassification	—Unverified	0
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations	Oct 4, 2021	Acoustic Scene ClassificationFairness	—Unverified	0
Towards Robust Domain Generalization in 2D Neural Audio Processing	Sep 29, 2021	Acoustic Scene ClassificationDomain Generalization	—Unverified	0
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification	Aug 11, 2021	Acoustic Scene ClassificationScene Classification	—Unverified	0
Robust Acoustic Scene Classification in the Presence of Active Foreground Speech	Aug 2, 2021	Acoustic Scene ClassificationScene Classification	—Unverified	0
Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint	Jul 30, 2021	Acoustic Scene ClassificationScene Classification	—Unverified	0
Over-Parameterization and Generalization in Audio Classification	Jul 19, 2021	Acoustic Scene ClassificationAudio Classification	—Unverified	0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification	Jul 3, 2021	Acoustic Scene ClassificationData Augmentation	—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