8 accepted papers
In recent years, dynamic parameterization of acoustic environments has garnered attention in audio processing. This focus includes room volume and reverberation time (RT<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</inf>), which defines local acous…
In recent years, dynamic parameterization of acoustic environments has raised increasing attention in the field of audio processing. One of the key parameters that characterize the local room acoustics in isolation from orientation and directivity of sources and receivers is the geometric room volum…
In target speaker extraction, many studies rely on the speaker embedding which is obtained from an enrollment of the target speaker and employed as the guidance. However, solely using speaker embedding may not fully utilize the contextual information contained in the enrollment. In this paper, we di…
In this paper, a method for estimating the autoregressive parameters from a signal segment is proposed. The method is based on a deep neural network (DNN) in combination with the classical Levinson-Durbin recursion (LDR). The DNN acts as a pre-processor for the LDR and can be trained on different me…
This paper proposes a linear prediction-based part-defined auto-encoder (PAE) network to enhance speech signal. The PAE is a defined decoder or a defined encoder network, based on efficient learning algorithm or classical model. In this paper, the PAE utilizes AR-Wiener filter as decoder part, and t…
This paper presents a novel approach for estimating autoregressive (AR) model parameters using deep neural network (DNN) in the AR-Wiener filtering speech enhancement. Unlike conventional DNN that predicts one kind of target, the DNN used in this paper is trained to predict the AR model parameters o…
This paper presents a novel technique for estimating autoregressive (AR) parameters of speech and noise in the codebook-driven Wiener filtering speech enhancement method. We only train the shape codebook of speech spectrum offline, and the shape of noise spectrum is estimated online for solving the…
We propose a sparse hidden Markov model (HMM)-based single-channel speech enhancement method that models the speech and noise gains accurately in both stationary and nonstationary environments. The objective function is augmented with an lp regularization term resulting in a sparse autoregressive HM…