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

In this paper, we propose an effective and robust method for acoustic sceneanalysis based on spatial information extracted from partially synchronizedand/or closely located distributed microphones. In the proposed method, toextract spatial information from distributed microphones while taking intoaccount whether any pairs of microphones are synchronized and/or closelylocated, we derive a new cepstrum feature utilizing a graph-based basistransformation. Specifically, in the proposed graph-based cepstrum, thelogarithm of the amplitude in a multichannel observation is converted to afeature vector by an inverse graph Fourier transform, which can considerwhether any pair of microphones is connected. Our experimental results indicatethat the proposed graph-based cepstrum effectively extracts spatial informationwith consideration of the microphone connections. Moreover, the results showthat the proposed method more robustly classifies acoustic scenes thanconventional spatial features when the observed sounds have a largesynchronization mismatch between partially synchronized microphone groups.

Acoustic Scene Analysis Using Partially Connected Microphones Based on Graph Cepstrum