Environmental Sound Classification
23 papers with code • 3 benchmarks • 6 datasets
Classification of Environmental Sounds. Most often sounds found in Urban environments. Task related to noise monitoring.
Benchmarks
These leaderboards are used to track progress in Environmental Sound Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ESC-50
UrbanSound8K
FSD50K
SONYC-UST-V2
ESC50
Latest papers
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99. 75\%, 93. 4\%, and 86. 49\% respectively.
Comparison of semi-supervised deep learning algorithms for audio classification
Labbeti/SSLH
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In all but one cases, MM, RMM, and FM outperformed MT and DCT significantly, MM and RMM being the best methods in most experiments.
Urban Sound Classification : striving towards a fair comparison
multitel-ai/urban-sound-classification-and-comparison
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Sometimes authors copy-pasting the results of the original papers which is not helping reproducibility.
CRNNs for Urban Sound Tagging with spatiotemporal context
multitel-ai/urban-sound-tagging
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This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge.
Rethinking CNN Models for Audio Classification
kamalesh0406/Audio-Classification
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Besides, we show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model.
ESResNet: Environmental Sound Classification Based on Visual Domain Models
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years.
Urban Sound Tagging using Convolutional Neural Networks
sainathadapa/urban-sound-tagging
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The proposed model uses log-scaled Mel-spectrogram as the representation format for the audio data.
Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks
jonnor/ESC-CNN-microcontroller
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n/a 2019
Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems.
End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network
Logan97117/environmental_sound_classification_1DCNN
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In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal.
Ubicoustics: Plug-and-Play Acoustic Activity Recognition
Despite sound being a rich source of information, computing devices with microphones do not leverage audio to glean useful insights about their physical and social context.
