SOTAVerified

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 76–100 of 132 papers

TitleStatusHype
SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification—0
Environmental sound analysis with mixup based multitask learning and cross-task fusion—0
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency DampingCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and eventsCode1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile ApplicationCode1
Deep Learning Based Open Set Acoustic Scene Classification—0
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification—0
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances—0
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification—0
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data AugmentationCode1
Capturing scattered discriminative information using a deep architecture in acoustic scene classification—0
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
A Transformer-based Audio Captioning Model with Keyword Estimation—0
Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions—0
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification—0
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching—0
Acoustic Scene Classification using Audio Tagging—0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional NetworksCode1
Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network—0
Neural Architecture Search on Acoustic Scene Classification—0
Environmental Sound Classification with Parallel Temporal-spectral Attention—0
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting—0
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All datasetsCochlSceneDCASE 2019 MobileTAU Urban Acoustic Scenes 2019TUT Acoustic Scenes 2017TUT Urban Acoustic Scenes 2018

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Audio Flamingo1:1 Accuracy0.83—Unverified
2Qwen-Audio1:1 Accuracy0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Basic + Spectrum CorrectionAccuracy70.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Two-stage ensemble system1:1 Accuracy81.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Qwen-Audio1:1 Accuracy0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1ERGL: event relational graph representation learningAcc78.1—Unverified