environmental sound classification with convolutional neural networks github

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ENVIRONMENTAL SOUND CLASSIFICATION WITH CONVOLUTIONAL NEURAL

This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectro-grams) with deltas

What is convolutional neural network?
Convolutional neural network, as one of the most commonly used architectures in deep learning, can learn in time and frequency simultaneously through convolution operation, which solves the limitations of traditional machine learning algorithm.
How accurate are convolutional models for environmental sound classification?
Dai, Dai, Qu, Li, and Das (2017) proposed several very deep convolutional models for environmental sound classification that achieved 72% of accuracy on UrbanSound8k dataset. The proposed models consist of batch normalization, residual learning, and down-sampling in the initial layers of the CNN.
Can convolutional neural networks classify short audio clips of environmental sounds?
Abstract: This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas.
Can deep convolutional neural networks learn discriminative spectro-temporal patterns?
The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models.

Convolutional Neural Networks (CNNs) explained

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