16 accepted papers
We introduce a real-time, multichannel speech enhancement algorithm which maintains the spatial cues of stereo recordings including two speech sources. Recognizing that each source has unique spatial information, our method utilizes a dual-path structure, ensuring the spatial cues remain unaffected…
In this paper, we propose a dereverberation and speech source separation method based on deep neural network (DNN). Unlike the cascade connection of dereverberation and speech source separation, the proposed method performs dereverberation and speech source separation jointly by a unified convolutiv…
We propose a new algorithm for joint dereverberation and blind source separation (DR-BSS). Our work builds upon the IRLMA-T framework that applies a unified filter combining dereverberation and separation. One drawback of this framework is that it requires several matrix inversions, an operation inh…
We propose a generalized formulation of direction of arrival estimation that includes many existing methods such as steered response power, subspace, coherent and incoherent, as well as speech sparsity-based methods. Unlike most conventional methods that rely exclusively on grid search, we introduce…
We propose to learn surrogate functions of universal speech priors for determined blind speech separation. Deep speech priors are highly desirable due to their superior modelling power, but are not compatible with state-of-the-art independent vector analysis based on majorization-minimization (AuxIV…
This paper proposes a deep neural network (DNN)–based multichannel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal. DNN-based multi-channel speech enhancement is often conducted in the time-frequency (T-F) domain because spatial filterin…
In this investigation, a deep neural network (DNN) based speech extraction method is proposed to enhance a speech signal propagating from the desired direction. The proposed method integrates knowledge based on a sound propagation model and the time-varying characteristics of a speech source, into a…
In this paper, we propose a joint training of two deep neural networks (DNNs) for dereverberation and speech source separation. The proposed method connects the first DNN, the dereverberation part, the second DNN, and the speech source separation part in a cascade manner. The proposed method does no…
In this paper, we propose a joint multi-channel speech source separation and dereverberation method in which multiple speech sources and late reverberation are separated in an unsupervised manner. The proposed method jointly optimizes an auto-regressive (AR) model based speech dereverberation and a…
In this paper, we propose scene-dependent acoustic event detection (AED) with scene conditioning and fake-scene-conditioned loss. The proposed method employs a multitask network, that has not only AED part but also acoustic scene classification (ASC). The scenes predicted by ASC are employed as an a…
In this paper, we propose a multi-channel speech source separation method with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an alternative to a clean signal, the proposed method adopts an estimated speech signal by an unsupervised speech sour…
A multi-channel speech source separation with a deep neural network which optimizes not only the time-varying variance of a speech source but also the multi-channel spatial covariance matrix jointly without any iterative optimization method is shown. Instead of a loss function which does not evaluat…
In this paper, we propose a block-online multi-channel speech enhancement technique which simultaneously optimizes time-frequency masks and forgetting factors for estimation of multichannel covariance matrices of the desired speech signal and the noise signal so as to maximize speech enhancement per…
In this paper, a multi-channel deep clustering technique which combines two types of spatial information is proposed. The first one is an estimated direction-of-arrival (DOA) at each time-frequency point, which is utilized as an input feature of the proposed neural network. Instead of stacking embed…
A new method for solving adaptive Boolean compressive sensing is proposed. By greedy maximization of an expected information gain, a conventional method controls the pool-size for adaptive Boolean compressive sensing. However, the conventional greedy method has the drawback that it has no guarantee…
In this paper, we propose a simultaneous optimization technique for speech dereverberation, acoustic echo reduction, and noise reduction, which can be utilized even when an analog-to-digital (A/D) converter and a digital-to-analog (D/A) converter are not synchronized. The proposed method utilizes a…