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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–132 of 132 papers

TitleStatusHype
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices—0
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances—0
An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network—0
An evaluation of data augmentation methods for sound scene geotagging—0
A punishment voting algorithm based on super categories construction for acoustic scene classification—0
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition—0
A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)—0
A Transformer-based Audio Captioning Model with Keyword Estimation—0
Attentive max feature map and joint training for acoustic scene classification—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a preliminary study—0
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori—0
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification—0
Capturing scattered discriminative information using a deep architecture in acoustic scene classification—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting—0
Improving Acoustic Scene Classification with City Features—0
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions—0
Compressing audio CNNs with graph centrality based filter pruning—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
Cross-task pre-training for on-device acoustic scene classification—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations—0
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Deep Learning Based Open Set Acoustic Scene Classification—0
Deep Neural Decision Forest for Acoustic Scene Classification—0
Deep Space Separable Distillation for Lightweight Acoustic Scene Classification—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—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