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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 11–20 of 132 papers

Title	Date	Tasks	Status	Hype
A Two-Stage Approach to Device-Robust Acoustic Scene Classification	Nov 3, 2020	Acoustic Scene ClassificationClassification	CodeCode Available	1
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events	Sep 21, 2020	Acoustic Scene ClassificationAudio Tagging	CodeCode Available	1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application	Aug 21, 2020	Acoustic Scene ClassificationData Augmentation	CodeCode Available	1
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation	Jul 16, 2020	Acoustic Scene ClassificationData Augmentation	CodeCode Available	1
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks	Mar 3, 2020	Acoustic Scene ClassificationGPU	CodeCode Available	1
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs	Oct 28, 2019	Acoustic Scene ClassificationScene Classification	CodeCode Available	1
Receptive-field-regularized CNN variants for acoustic scene classification	Sep 5, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	1
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification	Jul 3, 2019	Acoustic Scene ClassificationGeneral Classification	CodeCode Available	1
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge	May 3, 2025	Acoustic Scene ClassificationScene Classification	CodeCode Available	0
Improving Acoustic Scene Classification with City Features	Mar 21, 2025	Acoustic Scene ClassificationClassification	—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