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

Previous methods for predicting room acoustic parameters and speech qualitymetrics have focused on the single-channel case, where room acoustics and MeanOpinion Score (MOS) are predicted for a single recording device. However,quality-based device selection for rooms with multiple recording devices maybenefit from a multi-channel approach where the descriptive metrics arepredicted for multiple devices in parallel. Following our hypothesis that amodel may benefit from multi-channel training, we develop a multi-channel modelfor joint MOS and room acoustics prediction (MOSRA) for five channels inparallel. The lack of multi-channel audio data with ground truth labelsnecessitated the creation of simulated data using an acoustic simulator withroom acoustic labels extracted from the generated impulse responses and labelsfor MOS generated in a student-teacher setup using a wav2vec2-based MOSprediction model. Our experiments show that the multi-channel model improvesthe prediction of the direct-to-reverberation ratio, clarity, and speechtransmission index over the single-channel model with roughly 5$\times$ lesscomputation while suffering minimal losses in the performance of the othermetrics.

Multi-Channel MOSRA: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and a Teacher Model