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 51–75 of 132 papers

TitleStatusHype
Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions—0
Acoustic Scene Classification using Audio Tagging—0
Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network—0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge—0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration—0
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
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Neural Architecture Search on Acoustic Scene Classification—0
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2—0
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers—0
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations—0
On The Effect Of Coding Artifacts On Acoustic Scene Classification—0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
Over-Parameterization and Generalization in Audio Classification—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification—0
Robust Acoustic Scene Classification in the Presence of Active Foreground Speech—0
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification—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