Classification of Environmental Sounds. Most often sounds found in Urban environments. Task related to noise monitoring.
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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.
Ranked #1 on Environmental Sound Classification on UrbanSound8k
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.
Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems.
Ranked #3 on Environmental Sound Classification on UrbanSound8k
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.
End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations.
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years.
This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge.
We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds.