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.

Most implemented papers

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

justinsalamon/UrbanSound8K-JAMS 15 Aug 2016

We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation.

AudioCLIP: Extending CLIP to Image, Text and Audio

AndreyGuzhov/AudioCLIP 24 Jun 2021

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.

End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network

sajabdoli/Environmental_sound_classification 18 Apr 2019

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.

Rethinking CNN Models for Audio Classification

kamalesh0406/Audio-Classification 22 Jul 2020

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.

Masked Conditional Neural Networks for Environmental Sound Classification

fadymedhat/YorNoise 25 May 2018

We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds.

CRNNs for Urban Sound Tagging with spatiotemporal context

multitel-ai/urban-sound-tagging 24 Aug 2020

This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge.

Utilizing Domain Knowledge in End-to-End Audio Processing

corticph/MSTmodel 1 Dec 2017

End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations.

Ubicoustics: Plug-and-Play Acoustic Activity Recognition

FIGLAB/ubicoustics 14 Oct 2018

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.

Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks

jonnor/ESC-CNN-microcontroller n/a 2019

Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems.