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

Deep generative models can generate high-fidelity audio conditioned onvarious types of representations (e.g., mel-spectrograms, Mel-frequencyCepstral Coefficients (MFCC)). Recently, such models have been used tosynthesize audio waveforms conditioned on highly compressed representations.Although such methods produce impressive results, they are prone to generateaudible artifacts when the conditioning is flawed or imperfect. An alternativemodeling approach is to use diffusion models. However, these have mainly beenused as speech vocoders (i.e., conditioned on mel-spectrograms) or generatingrelatively low sampling rate signals. In this work, we propose a high-fidelitymulti-band diffusion-based framework that generates any type of audio modality(e.g., speech, music, environmental sounds) from low-bitrate discreterepresentations. At equal bit rate, the proposed approach outperformsstate-of-the-art generative techniques in terms of perceptual quality. Trainingand, evaluation code, along with audio samples, are available on thefacebookresearch/audiocraft Github page.

From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion