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

Showing 1120 of 132 papers

TitleStatusHype
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data AugmentationCode1
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural NetworksCode1
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional NetworksCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile ApplicationCode1
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and eventsCode1
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration0
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Benchmark Results

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