  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. In addition, we investigate the impact of data augmentation
techniques and transfer learning on the performance of our systems. Our best
system achieves a mean average precision (mAP) of 0.450 on the AudioSet
dataset. Although this value is less than that of the state-of-the-art system,
the proposed system is 7.1x faster and 9.7x smaller. On the ESC-50,
UrbanSound8K, and RAVDESS datasets, we obtain state-of-the-art results with
accuracies of 0.961, 0.908, and 0.748, respectively. Our system for the ESC-50
dataset is 1.7x faster and 2.3x smaller than the previous best system. For the
RAVDESS dataset, our system is 3.3x smaller than the previous best system. We
name our systems "Efficient Residual Audio Neural Networks".
