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
Face: Fast, Accurate and Context-Aware Audio Annotation and Classification
This paper presents a context-aware framework for feature selection and classification procedures to realize a fast and accurate audio event annotation and classification.
Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block
JNAIC/PIPMN
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Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification.
Audio Barlow Twins: Self-Supervised Audio Representation Learning
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision.
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.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
PaddlePaddle/DeepSpeech
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NAACL (ACL) 2022
PaddleSpeech is an open-source all-in-one speech toolkit.
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.
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.
AudioCLIP: Extending CLIP to Image, Text and Audio
AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90. 07% on the UrbanSound8K and 97. 15% on the ESC-50 datasets.
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.
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).
