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. However, many of the existing approaches …
and has seen a lot of progress in the past years. However, many of the existing approaches …
Urban sound classification using long short-term memory neural network
Environmental sound classification has received more attention in recent years. Analysis of
environmental sounds is difficult because of its unstructured nature. However, the presence …
environmental sounds is difficult because of its unstructured nature. However, the presence …
Urban sound classification using convolutional neural network and long short term memory based on multiple features
There are many sounds all around us and our brain can easily and clearly identify them.
Furthermore, our brain processes the received sound signals continuously and provides us …
Furthermore, our brain processes the received sound signals continuously and provides us …
Esresne (x) t-fbsp: Learning robust time-frequency transformation of audio
Environmental Sound Classification (ESC) is a rapidly evolving field that recently
demonstrated the advantages of application of visual domain techniques to the audio …
demonstrated the advantages of application of visual domain techniques to the audio …
Unsupervised feature learning for environmental sound classification using weighted cycle-consistent generative adversarial network
In this paper we propose a novel environmental sound classification approach incorporating
unsupervised feature learning via the spherical K-Means++ algorithm and a new …
unsupervised feature learning via the spherical K-Means++ algorithm and a new …
An automated environmental sound classification methods based on statistical and textural feature
E Akbal - Applied Acoustics, 2020 - Elsevier
Determining the location from environmental sounds is crucial for digital forensics.
Therefore, it is possible to predict about the sounds obtained using the automatic …
Therefore, it is possible to predict about the sounds obtained using the automatic …
A deep attention model for environmental sound classification from multi-feature data
J Guo, C Li, Z Sun, J Li, P Wang - Applied Sciences, 2022 - mdpi.com
Automated environmental sound recognition has clear engineering benefits; it allows audio
to be sorted, curated, and searched. Unlike music and language, environmental sound is …
to be sorted, curated, and searched. Unlike music and language, environmental sound is …
EnViTSA: ensemble of vision transformer with SpecAugment for acoustic event classification
Recent successes in deep learning have inspired researchers to apply deep neural
networks to Acoustic Event Classification (AEC). While deep learning methods can train …
networks to Acoustic Event Classification (AEC). While deep learning methods can train …
Multi-representation knowledge distillation for audio classification
L Gao, K Xu, H Wang, Y Peng - Multimedia Tools and Applications, 2022 - Springer
Audio classification aims to discriminate between different audio signal types, and it has
received intensive attention due to its wide applications. In deep learning-based audio …
received intensive attention due to its wide applications. In deep learning-based audio …
TimeScaleNet: A multiresolution approach for raw audio recognition using learnable biquadratic IIR filters and residual networks of depthwise-separable one …
E Bavu, A Ramamonjy, H Pujol… - IEEE Journal of Selected …, 2019 - ieeexplore.ieee.org
In this paper, we show the benefit of a multi-resolution approach that allows us to encode the
relevant information contained in unprocessed time-domain acoustic signals. TimeScaleNet …
relevant information contained in unprocessed time-domain acoustic signals. TimeScaleNet …