Blind Estimation of Room Acoustic Parameters and Speech Transmission Index using MTF-based CNNs

Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation

The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications especially in highly reverberant environments, and the natural gradient makes it hard to balance well between fast convergence speed and stability. In this paper, we propose two more effective SBSS methods based on auxiliary-function-based independent vector analysis (AuxIVA) and independent low-rank ma…

注意力即一切：用于盲房间体积估计

Attention Is All You Need For Blind Room Volume Estimation

本文提出一种纯注意力Transformer模型，利用Gammatone幅度谱和相位谱输入，结合跨模态迁移学习，实现单通道带噪语音的盲房间体积估计，性能优于传统CNN。

Blind Acoustic Room Parameter Estimation Using Phase Features

Modeling room acoustics in a field setting involves some degree of blind parameter estimation from noisy and reverberant audio. Modern approaches leverage convolutional neural networks (CNNs) in tandem with time-frequency representation. Using short-time Fourier transforms to develop these spectrogram-like features has shown promising results, but this method implicitly discards a significant amount of audio information in the phase domain. Inspired by recent works in speech enhancement, we propose utilizing novel phase-related features to extend recent approaches to blindly estimate the so-ca…