Evaluating the Non-Intrusive Room Acoustics Algorithm with the ACE Challenge
This work addresses the challenge of room acoustics parameter estimation for audio processing applications, presenting an incremental improvement with specific performance metrics.
The paper tackled the problem of non-intrusively estimating full-band reverberation time (T60) and direct-to-reverberant ratio (DRR) from single-channel reverberant speech, achieving a Root Mean Square Deviation (RMSD) of 3.84 dB for DRR and 43.19% for T60 estimation.
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.