17 accepted papers
Subspace clustering is a popular method for clustering unlabelled data. However, the computational cost of the subspace clustering algorithm can be unaffordable when dealing with a large data set. Using a set of dimension sketched data instead of the original data set can be helpful for mitigating t…
In this paper, a robust Student’s-T distribution aided One-Stage Orientation detector, namely TOSO, is proposed to address orientation target detection in remote sensing images. A one-stage keypoint based network architecture is used to avoid the complicated computation caused by rotation anchor box…
The selection of step sizes in the progressive Gaussian approximate filter (PGAF) is important, and it is difficult to select optimal values in practical applications. Furthermore, in the PGAF, significant integral approximation errors are generated by the repeated approximate calculations of the Ga…
Labelled data are often required to train an acoustic scene classification system. However, it is time-consuming and expensive to label the data manually. An unsupervised clustering algorithm can be used to facilitate the labelling process by dividing the acoustic data into different categories. Nev…
Given the high demand for automated systems for human action recognition, great efforts have been undertaken in recent decades to progress the field. In this paper, we present frameworks for single and multi-viewpoints action recognition based on Space-Time Volume (STV) of human silhouettes and 3D-H…
For a linear sensor array, using line spectra is a common technique for estimating directions of arrival (DOA) of single-tone sources. Yet, very few papers consider multitone sources. For the first time, we provide the optimal Bayesian inference for multi-line spectra, i.e. a superposition of line s…
In this paper, we propose deep discriminative correlation matching within the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for online multiple human tracking. In this matching scheme, we mainly exploit the Convolutional Neural Network (CNN) based Discriminative Correlation Filter…
The performance of deep neural network (DNN) based monaural speech separation methods is limited in reverberant and noisy room environments. In this paper, we propose a new DNN training target which incorporates geometric information describing the target speaker and microphone to improve the perfor…
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been shown to be promising for audio-visual multi-speaker tracking. Recently, the zero diffusion particle flow (ZPF) has been used to mitigate the weight degeneracy problem in the SMC-PHD filter. However, this leads to a…
Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propos…
Time-frequency (T-F) masking algorithms are focused at separating multiple sound sources from binaural reverberant speech mixtures. The statistical modelling of binaural cues i.e. interaural phase difference (IPD) and interaural level difference (ILD) is a significant aspect of such algorithms. In t…
The scale matrix and degrees of freedom (dof) parameter of a Student's t distribution are important for nonlinear robust inference, and it is difficult to determine exact values in practical application due to complex environments. To solve this problem, an improved robust Gaussian approximate (GA)…
In this paper, we propose a novel robust multiple human tracking approach based upon processing a video signal by utilizing a social force model to enhance the particle probability hypothesis density (PHD) filter. In traditional dynamic models, the states of targets are only predicted by their own h…
Estimating the geometric properties of an indoor environment through acoustic room impulse responses (RIRs) is useful in various applications, e.g., source separation, simultaneous localization and mapping, and spatial audio. Previously, we developed an algorithm to estimate the reflector's position…
The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distrib…
A common approach to blind source separation is to use independent component analysis. However when dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in ind…
The model-based expectation maximization source separation and localization (MESSL) technique is a probabilistic time-frequency masking algorithm that achieves underdetermined blind source separation of speech sources. Using only two-channel recordings, MESSL clusters spectrogram points based on the…