English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Audio tagging aims to detect the types of sound events occurring in an audiorecording. To tag the polyphonic audio recordings, we propose to useConnectionist Temporal Classification (CTC) loss function on the top ofConvolutional 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 oflabels to clip-level probability of labels. To compare the mapping ability ofGLU-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 wealso compare the proposed GLU-CTC system with the baseline system, which is aCRNN trained using CTC loss function without GLU. The experiments show that theGLU-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 of0.837. That means based on the same CRNN model with GLU, the performance of CTCmapping is better than the GMP and GAP mapping. Given both based on the CTCmapping, the CRNN with GLU outperforms the CRNN without GLU.

Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units