2021

ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition

Verbitskiy, Sergey, Berikov, Vladimir, Vyshegorodtsev, Viacheslav

Understand

Audio pattern recognition (APR) is an important research topic and can be applied to several fields related to our lives.

Therefore, accurate and efficient APR systems need to be developed as they are useful in real applications.
In this paper, we propose a new convolutional neural network (CNN) architecture and a method for improving the inference speed of CNN-based systems for APR tasks.
Moreover, using the proposed method, we can improve the performance of our systems, as confirmed in experiments conducted on four audio datasets.

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