  Audio classification can distinguish different kinds of sounds, which is
helpful for intelligent applications in daily life. However, it remains a
challenging task since the sound events in an audio clip is probably multiple,
even overlapping. This paper introduces an end-to-end audio classification
system based on raw waveforms and mix-training strategy. Compared to
human-designed features which have been widely used in existing research, raw
waveforms contain more complete information and are more appropriate for
multi-label classification. Taking raw waveforms as input, our network consists
of two variants of ResNet structure which can learn a discriminative
representation. To explore the information in intermediate layers, a
multi-level prediction with attention structure is applied in our model.
Furthermore, we design a mix-training strategy to break the performance
limitation caused by the amount of training data. Experiments show that the
mean average precision of the proposed audio classification system on Audio Set
dataset is 37.2%. Without using extra training data, our system exceeds the
state-of-the-art multi-level attention model.
