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 101–125 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
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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