A Geometric Approach to Sound Source Localization from Time-Delay Estimates

This paper addresses the problem of sound-source localization from time-delay estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel geometric formulation is proposed, together with a thorough algebraic analysis and a global optimization solver. The proposed model is thoroughly described and evaluated. The geometric analysis, stemming from the direct acoustic propagation model, leads to necessary and sufficient conditions for a set of time delays to correspond to a unique position in the source space. Such sets of time delays are referred to as feasible sets. We formally prove that every feasible set corresponds to exactly one position in the source space, whose value can be recovered using a closed-form localization mapping. Therefore we seek for the optimal feasible set of time delays given, as input, the received microphone signals. This time delay estimation problem is naturally cast into a programming task, constrained by the feasibility conditions derived from the geometric analysis. A global branch-and-bound optimization technique is proposed to solve the problem at hand, hence estimating the best set of feasible time delays and, subsequently, localizing the sound source. Extensive experiments with both simulated and real data are reported; we compare our methodology to four state-of-the-art techniques. This comparison clearly shows that the proposed method combined with the branch-and-bound algorithm outperforms existing methods. These in-depth geometric understanding, practical algorithms, and encouraging results, open several opportunities for future work.Comment: 13 pages, 2 figures, 3 table, journa

Iterative synchronisation and DC-offset estimation using superimposed training

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in [2],[4] and [5], due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data “noise”. The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation

Characterisation of denitrification in the subsurface environment of the Manawatū catchment, New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand

Figures 2.1 & 2.2 have been removed for copyright reasons but may be accessed via their source listed in the References (Rivett et al., 2008, Fig. 2 & Saggar et al., 2013, Fig. 3).A sound understanding of the quantity of nitrate lost from agricultural soils, as well as their transport and transformation in soil-water systems is essential for targeted and effective management and/or mitigation of their impacts on the quality of receiving waters. However, there is currently little known about the occurrence, variability, or factors affecting, nitrate attenuation by subsurface (below the root zone) denitrification in New Zealand, particularly in the Manawatū River catchment. This thesis developed and applied a combination of regional- and local-scale hydrogeochemical surveys and experiments, to gain an insight into the occurrence, variability, and hydrogeological features of subsurface denitrification in the Manawatū River catchment, particularly in the Tararua Groundwater Management Zone (GWMZ). A regional survey and analysis of samples from 56 groundwater wells conducted in the Tararua GWMZ revealed mainly oxic groundwater with low denitrification potential in the southern part of the catchment (Mangatainoka sub-catchment), whereas mainly anoxic/reduced groundwaters with high potential to denitrify in the middle and northern parts (Upper Manawatū sub-catchments). Oxic groundwaters with enriched nitrate concentrations were generally correlated with coarse textured soil types and aquifer materials (e.g., well-drained soil, gravel rock type), allowing faster movement of percolating water and oxygen diffusion from surface to subsurface environments. Local-scale laboratory incubations and in-field, push-pull test techniques were evaluated and optimised to measure and quantify denitrification in unsaturated (vadose) and saturated (shallow groundwater) parts of the subsurface environment. A novel incubation technique using vacuum pouches was found to be more reliable than traditional Erlenmeyer flasks in determining denitrifying enzyme activity (DEA) in subsurface soils (>0.3 m depth) with low denitrification activity. A combination of 75 μg N g-1 dry soil and 400 μg C g-1 dry soil was also found to provide the optimum DEA in subsurface soils. In the evaluation of the push-pull test, denitrification rates estimated using the measurements of denitrification reactant (nitrate) were found to be significantly higher (6 to 60 times) as compared to the rates estimated using the measurements of denitrification product (nitrous oxide). The estimates of denitrification rates also differed depending on whether a zero-order or first-order kinetic model was assumed. However, either a zero-order or a first-order model appears to be valid to estimate the denitrification rate from push-pull test data. The optimised laboratory incubation technique and in-field, push-pull test were applied at four sites with contrasting redox properties; Palmerston North, Pahiatua, Woodville, and Dannevirke. The incubation technique revealed that denitrification potential in terms of DEA is highest in the surface soil and generally decreased with soil depth. The push-pull test measured large denitrification rates of 0.04 to 1.07 mg N L-1 h-1 in the reduced groundwaters at depths of 4.5-7.5 m below ground level at two of the sites (Woodville and Palmerston North), whereas there were no clear indications of denitrification in the oxidised shallow groundwaters at the other two sites (Pahiatua and Dannevirke). This new knowledge, information and techniques advance our scientific capability to assess and map subsurface denitrification potential for targeted and effective land use planning and water quality measures in the Manawatū catchment and other catchments across New Zealand’s agricultural landscapes and worldwide

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

Block synchronisation for joint channel and DC-offset estimation using data-dependent superimposed training

In this paper, we propose a new (single-step) block synchronisation algorithm for joint channel and DC-offset estimation for data-dependent superimposed training (DDST). While a (two-step) block synchronisation algorithm for DDST has previously been proposed in [5], due to interference from the information-bearing data it performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were then estimated in [5] in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by exploiting the special structure of the channel output’s cyclic mean vector. The result is a BER performance superior to the DDST synchronisation algorithm first published in [5]

EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis

Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-maximization (EM). In this paper we propose a new mixture model that associates a weight with each observed point. We introduce the weighted-data Gaussian mixture and we derive two EM algorithms. The first one considers a fixed weight for each observation. The second one treats each weight as a random variable following a gamma distribution. We propose a model selection method based on a minimum message length criterion, provide a weight initialization strategy, and validate the proposed algorithms by comparing them with several state of the art parametric and non-parametric clustering techniques. We also demonstrate the effectiveness and robustness of the proposed clustering technique in the presence of heterogeneous data, namely audio-visual scene analysis.Comment: 14 pages, 4 figures, 4 table

Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments

We address the problem of online localization and tracking of multiple moving speakers in reverberant environments. The paper has the following contributions. We use the direct-path relative transfer function (DP-RTF), an inter-channel feature that encodes acoustic information robust against reverberation, and we propose an online algorithm well suited for estimating DP-RTFs associated with moving audio sources. Another crucial ingredient of the proposed method is its ability to properly assign DP-RTFs to audio-source directions. Towards this goal, we adopt a maximum-likelihood formulation and we propose to use an exponentiated gradient (EG) to efficiently update source-direction estimates starting from their currently available values. The problem of multiple speaker tracking is computationally intractable because the number of possible associations between observed source directions and physical speakers grows exponentially with time. We adopt a Bayesian framework and we propose a variational approximation of the posterior filtering distribution associated with multiple speaker tracking, as well as an efficient variational expectation-maximization (VEM) solver. The proposed online localization and tracking method is thoroughly evaluated using two datasets that contain recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201