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

Recently, the ability of language models (LMs) has attracted increasingattention in visual cross-modality. In this paper, we further explore thegeneration capacity of LMs for sound event detection (SED), beyond the visualdomain. Specifically, we propose an elegant method that aligns audio featuresand text features to accomplish sound event classification and temporallocation. The framework consists of an acoustic encoder, a contrastive modulethat align the corresponding representations of the text and audio, and adecoupled language decoder that generates temporal and event sequences from theaudio characteristic. Compared with conventional works that require complicatedprocessing and barely utilize limited audio features, our model is more conciseand comprehensive since language model directly leverage its semanticcapabilities to generate the sequences. We investigate different decouplingmodules to demonstrate the effectiveness for timestamps capture and eventclassification. Evaluation results show that the proposed method achievesaccurate sequences of sound event detection.

Furnishing Sound Event Detection with Language Model Abilities