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 81–90 of 132 papers

TitleStatusHype
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
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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