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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 101–110 of 132 papers

Title	Date	Tasks	Status	Hype
Neural Architecture Search on Acoustic Scene Classification	Dec 30, 2019	Acoustic Scene ClassificationClassification	—Unverified	0
Environmental Sound Classification with Parallel Temporal-spectral Attention	Dec 14, 2019	Acoustic Scene ClassificationAudio Classification	—Unverified	0
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting	Nov 10, 2019	Acoustic Scene ClassificationGeneral Classification	—Unverified	0
Cross-task pre-training for on-device acoustic scene classification	Oct 22, 2019	Acoustic Scene ClassificationClassification	—Unverified	0
Acoustic Scene Classification Based on a Large-margin Factorized CNN	Oct 14, 2019	Acoustic Scene ClassificationClassification	—Unverified	0
Acoustic scene analysis with multi-head attention networks	Sep 16, 2019	Acoustic Scene ClassificationGeneral Classification	CodeCode Available	0
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
City classification from multiple real-world sound scenes	Jul 29, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling	Jul 15, 2019	Acoustic Scene ClassificationData Augmentation	—Unverified	0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge	Jul 13, 2019	Acoustic Scene ClassificationScene Classification	—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