Evaluating the Non-Intrusive Room Acoustics Algorithm with the ACE Challenge

2015/10/15 by Pablo Peso Parada, Dushyant Sharma, Parada, Pablo Peso +5
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Sound (cs.SD) #Speech and Audio Processing #cs.SD

paper · pdf · doi:10.48550/arxiv.1510.04616

In Proceedings of the ACE Challenge Workshop - a satellite event of IEEE-WASPAA 2015 (arXiv:1510.00383)

arxiv created 2015/10/15 · openalex publication_date 2015/10/15 · arxiv updated 2015/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a single channel data driven method for non-intrusive estimation of full-band reverberation time and full-band direct-to-reverberant ratio. The method extracts a number of features from reverberant speech and builds a model using a recurrent neural network to estimate the reverberant acoustic parameters. We explore three configurations by including different data and also by combining the recurrent neural network estimates using a support vector machine. Our best method to estimate DRR provides a Root Mean Square Deviation (RMSD) of 3.84 dB and a RMSD of 43.19 % for T60 estimation.

Citations

• Proceedings of the ACE Challenge Workshop - a satellite event of IEEE-WASPAA (2015)

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