  Music, speech, and acoustic scene sound are often handled separately in the
audio domain because of their different signal characteristics. However, as the
image domain grows rapidly by versatile image classification models, it is
necessary to study extensible classification models in the audio domain as
well. In this study, we approach this problem using two types of sample-level
deep convolutional neural networks that take raw waveforms as input and uses
filters with small granularity. One is a basic model that consists of
convolution and pooling layers. The other is an improved model that
additionally has residual connections, squeeze-and-excitation modules and
multi-level concatenation. We show that the sample-level models reach
state-of-the-art performance levels for the three different categories of
sound. Also, we visualize the filters along layers and compare the
characteristics of learned filters.
