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 101–132 of 132 papers

TitleStatusHype
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNsCode1
Cross-task pre-training for on-device acoustic scene classification—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
Acoustic scene analysis with multi-head attention networksCode0
Receptive-field-regularized CNN variants for acoustic scene classificationCode1
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene ClassificationCode0
City classification from multiple real-world sound scenesCode0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling—0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge—0
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene ClassificationCode1
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene ClassificationCode0
Acoustic Scene Classification by Implicitly Identifying Distinct Sound EventsCode0
Spatio-Temporal Attention Pooling for Audio Scene Classification—0
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification—0
Training neural audio classifiers with few dataCode0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions—0
Unsupervised adversarial domain adaptation for acoustic scene classificationCode0
Acoustic Scene Classification: A Competition Review—0
A multi-device dataset for urban acoustic scene classificationCode0
A punishment voting algorithm based on super categories construction for acoustic scene classification—0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network—0
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification—0
Hierarchical learning for DNN-based acoustic scene classification—0
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global PoolingCode0
Label Tree Embeddings for Acoustic Scene Classification—0
Acoustic Scene Classification—0
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a preliminary study—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