mlsanityData to 13 Oct 2026

Environment Classification via Blind Roomprints Estimation

arXiv 2209.07196 ↗First posted 15 Sep 2022eess.AS cs.SDcited by 2

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Abstract (arXiv)

In this paper we present a novel approach for environment classification for speech recordings, which does not require the selection of decaying reverberation tails. It is based on a multi-band RT60 analysis of blind channel estimates and achieves an accuracy of up to 93.6% on test recordings derived from the ACE corpus.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). First posted on arXiv 15 Sep 2022.

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Citing papers per year: 2024: 2.

2 papers.

  1. 2024EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environmentscited by 0
  2. 2024RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding using Contrastive Learning with Application to Room Shape Classificationcited by 0