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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning approaches for beat and downbeat tracking have broughtadvancements. However, these approaches continue to rely on hand-crafted,subsampled spectral features as input, restricting the information available tothe model. In this work, we propose WaveBeat, an end-to-end approach for jointbeat and downbeat tracking operating directly on waveforms. This method forgoesengineered spectral features, and instead, produces beat and downbeatpredictions directly from the waveform, the first of its kind for this task.Our model utilizes temporal convolutional networks (TCNs) operating onwaveforms that achieve a very large receptive field ($\geq$ 30 s) at audiosample rates in a memory efficient manner by employing rapidly growing dilationfactors with fewer layers. With a straightforward data augmentation strategy,our method outperforms previous state-of-the-art methods on some datasets,while producing comparable results on others, demonstrating the potential fortime domain approaches.

WaveBeat: End-to-end beat and downbeat tracking in the time domain