19 accepted papers
The speaker diarization task answers the question "who is speaking at a given time?". It represents valuable information for scene analysis in a domain such as robotics. In this paper, we introduce a temporal audio-visual fusion model for multiusers speaker diarization, with low computing requiremen…
This paper investigates the impact of head movements on audio-visual speech enhancement (AVSE). Although being a common conversational feature, head movements have been ignored by past and recent studies: they challenge today’s learning-based methods as they often degrade the performance of models t…
This paper proposes a full-band and sub-band fusion model, named as FullSubNet, for single-channel real-time speech enhancement. Full-band and sub-band refer to the models that input full-band and sub-band noisy spectral feature, output full-band and sub-band speech target, respectively. The sub-ban…
This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE). The deep generative speech model is trained using clean speech signals only, and it is combined with a nonnegative matrix factorization noise model for speech enhancement. We propose…
The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain sub-modules using loss functions that often do not correlate with establis…
In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian modeling framework. We propose to use a neural network for modeling the speech spectro-temporal content. The parameters of t…
This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders. The noise model remains unsupervised because we do not assume prior knowledge of the noisy recording environment. In t…
Multiple-speaker tracking is a crucial task for many applications. In real-world scenarios, exploiting the complementarity between auditory and visual data enables to track people outside the visual field of view. However, practical methods must be robust to changes in acoustic conditions, e.g. reve…
We address the problem of audio-visual gaze control in the specific context of human-robot interaction, namely how controlled robot motions are combined with visual and acoustic observations in order to direct the robot head towards targets of interest. The paper has the following contributions: (i)…
In this paper we address the problem of how to robustly train a ConvNet for regression, or deep robust regression. Traditionally, deep regression employ the L2 loss function, known to be sensitive to outliers, i.e. samples that either lie at an abnormal distance away from the majority of the trainin…
The multichannel inverse filtering method, i.e. multiple input/output inverse theorem (MINT), is widely used. However, it is usually performed in the time domain, and based on the long room impulse responses, thus it has a high computational complexity and a large number of near-common zeros. In thi…
We present a probabilistic model for joint source separation and diarisation of multichannel convolutive speech mixtures. We build upon the framework of local Gaussian model (LGM) with non-negative matrix factorization (NMF). The diarisation is introduced as a temporal labeling of each source in the…
This paper addresses the problem of under-determined convolutive audio source separation in a semi-oracle configuration where the mixing filters are assumed to be known. We propose a separation procedure based on the convolutive transfer function (CTF), which is a more appropriate model for strongly…
Convolutional Neural Networks (ConvNets) have become the state-of-the-art for many classification and regression problems in computer vision. When it comes to regression, approaches such as measuring the Euclidean distance of target and predictions are often employed as output layer. In this paper,…
Multi-person tracking with a robotic platform is one of the cornerstones of human-robot interaction. Challenges arise from occlusions, appearance changes and a time-varying number of people. Furthermore, the final system is constrained by the hardware platform: low computational capacity and limited…
In this paper we present a new statistical model for the power spectral density (PSD) of an audio signal and its application to multichannel audio source separation (MASS). The source signal is modeled with the local Gaussian model (LGM) and we propose to model its variance with an inverse-Gamma dis…
Estimating the noise power spectral density (PSD) is essential for single channel speech enhancement algorithms. In this paper, we propose a noise PSD estimation approach based on regional statistics. The proposed regional statistics consist of four features representing the statistics of the past a…
This paper addresses the problem of sound-source localization (SSL) with a robot head, which remains a challenge in real-world environments. In particular we are interested in locating speech sources, as they are of high interest for human-robot interaction. The microphone-pair response correspondin…
This paper addresses the problem of relative transfer function (RTF) estimation in the presence of stationary noise. We propose an RTF identification method based on segmental power spectral density (PSD) matrix subtraction. First multiple channel microphone signals are divided into segments corresp…