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Audio tagging aims to predict one or several labels in an audio clip. Manyprevious works use weakly labelled data (WLD) for audio tagging, where onlypresence or absence of sound events is known, but the order of sound events isunknown. To use the order information of sound events, we propose sequentiallabelled data (SLD), where both the presence or absence and the orderinformation of sound events are known. To utilize SLD in audio tagging, wepropose a Convolutional Recurrent Neural Network followed by a ConnectionistTemporal Classification (CRNN-CTC) objective function to map from an audio clipspectrogram to SLD. Experiments show that CRNN-CTC obtains an Area Under Curve(AUC) score of 0.986 in audio tagging, outperforming the baseline CRNN of 0.908and 0.815 with Max Pooling and Average Pooling, respectively. In addition, weshow CRNN-CTC has the ability to predict the order of sound events in an audioclip.

Audio Tagging With Connectionist Temporal Classification Model Using Sequential Labelled Data