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
Most implemented papers
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.
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 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.
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.
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.
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
mohaimenz/acdnet
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Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed, and lack of GPU support).
Tiny Transformers for Environmental Sound Classification at the Edge
With the growth of the Internet of Things and the rise of Big Data, data processing and machine learning applications are being moved to cheap and low size, weight, and power (SWaP) devices at the edge, often in the form of mobile phones, embedded systems, or microcontrollers.
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath
AUCO ResNet has proved to provide state of art results on many datasets.
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network
Alibaba-MIIL/AudioClassfication
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While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, fine-tuned from large datasets.
Continual Learning For On-Device Environmental Sound Classification
swagshaw/asc-cl
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Experimental results on the DCASE 2019 Task 1 and ESC-50 dataset show that our proposed method outperforms baseline continual learning methods on classification accuracy and computational efficiency, indicating our method can efficiently and incrementally learn new classes without the catastrophic forgetting problem for on-device environmental sound classification.
