Audio tagging aims to detect the types of sound events occurring in an audio
recording. To tag the polyphonic audio recordings, we propose to use
Connectionist Temporal Classification (CTC) loss function on the top of
Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units
(GLU-CTC), based on a new type of audio label data: Sequentially Labelled Data
(SLD). In GLU-CTC, CTC objective function maps the frame-level probability of
labels to clip-level probability of labels. To compare the mapping ability of
GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling
(GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we
also compare the proposed GLU-CTC system with the baseline system, which is a
CRNN trained using CTC loss function without GLU. The experiments show that the
GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging,
outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of
0.837. That means based on the same CRNN model with GLU, the performance of CTC
mapping is better than the GMP and GAP mapping. Given both based on the CTC
mapping, the CRNN with GLU outperforms the CRNN without GLU.Comment: DCASE2018 Workshop. arXiv admin note: text overlap with
arXiv:1808.0193
