Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation

Abstract

The reverberation time (T60) and the direct-to-reverberant ratio (DRR) are commonly used to characterize room acoustic environments. Both parameters can be measured from an acoustic impulse response (AIR) or using blind estimation methods that perform estimation directly from speech. When neural networks are used for blind estimation, however, a large realistic dataset is needed, which is expensive and time consuming to collect. To address this, we propose an AIR augmentation method that can parametrically control the T60 and DRR, allowing us to expand a small dataset of real AIRs into a balanced dataset orders of magnitude larger. Using this method, we train a previously proposed convolutional neural network (CNN) and show we can outperform past single-channel state-of-the-art methods. We then propose a more efficient, straightforward baseline CNN that is 4-5x faster, which provides an additional improvement and is better or comparable to all previously reported single-and multi-channel state-of-the-art methods.

Scene-Aware Audio Rendering via Deep Acoustic Analysis

“…Salamon et al used data augmentation to improve environmental sound classification [47]. Similarly, Bryan estimates the T 60 and the direct-toreverberant ratio (DRR) from a single speech recording via augmented datasets [5]. Tang et al trained CRNN models purely based on synthetic spatial IRs that generalize to real-world recordings [60].…”

“…Synthetic IRs are easy to obtain and can be used, but again lack wave-based effects as well as other simulation deficiencies. Recent work has addressed this issue by combining real-word IR measurements with augmentation to increase the diversity of existing real-world datasets [5]. This work, however, only addresses T 60 and DRR augmentation, and lacks a method to augment the frequencyequalization of existing IRs.…”

“…Most prior work takes the full-frequency range as input for prediction. For example, one closely related work [5] only predicts one T 60 value for the entire frequency range (full-band). However, sound propagates and interacts with materials differently at different frequencies.…”

“…First, we use the method in [5] to expand the T 60 and direct-toreverberant ratio (DRR) range of the 70 ACE IRs, resulting in 7000 synthetic IRs with a balanced T 60 distribution between 0.1-1.5 seconds. The ground truth T 60 estimates can be computed directly from IRs can be computed is a variety of ways.…”

“…The final output layer has 7 fully connected units to predict a vector of length 7 for T 60 or 6 fully connected units to predict a vector of length 6 for frequency equalization. This network architecture is inspired by Bryan [5], where it was used to predict full-band T 60 . We updated the output layer to predict the more challenging sub-band T 60 , and also discovered that the same architecture predicts equalization well.…”