Implementation Issues for acoustic echo cancellers

The high computational complexity of acoustic echo cancellation algorithms requires application specific implementations to sustain real time signal processing with affordable power consumption. This is especially true for systems where a delayless approach is considered important, e.g. wireless communication systems. The proposed paper presents architectural considerations to reach a feasible hardware solution

Algorithms and architectures for the multirate additive synthesis of musical tones

In classical Additive Synthesis (AS), the output signal is the sum of a large number of independently controllable sinusoidal partials. The advantages of AS for music synthesis are well known as is the high computational cost. This thesis is concerned with the computational optimisation of AS by multirate DSP techniques. In note-based music synthesis, the expected bounds of the frequency trajectory of each partial in a finite lifecycle tone determine critical time-invariant partial-specific sample rates which are lower than the conventional rate (in excess of 40kHz) resulting in computational savings. Scheduling and interpolation (to suppress quantisation noise) for many sample rates is required, leading to the concept of Multirate Additive Synthesis (MAS) where these overheads are minimised by synthesis filterbanks which quantise the set of available sample rates. Alternative AS optimisations are also appraised. It is shown that a hierarchical interpretation of the QMF filterbank preserves AS generality and permits efficient context-specific adaptation of computation to required note dynamics. Practical QMF implementation and the modifications necessary for MAS are discussed. QMF transition widths can be logically excluded from the MAS paradigm, at a cost. Therefore a novel filterbank is evaluated where transition widths are physically excluded. Benchmarking of a hypothetical orchestral synthesis application provides a tentative quantitative analysis of the performance improvement of MAS over AS. The mapping of MAS into VLSI is opened by a review of sine computation techniques. Then the functional specification and high-level design of a conceptual MAS Coprocessor (MASC) is developed which functions with high autonomy in a loosely-coupled master- slave configuration with a Host CPU which executes filterbanks in software. Standard hardware optimisation techniques are used, such as pipelining, based upon the principle of an application-specific memory hierarchy which maximises MASC throughput

Deep Riemannian Networks for EEG Decoding

State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning or Riemannian-Geometry-based decoders. Recently, there is growing interest in Deep Riemannian Networks (DRNs) possibly combining the advantages of both previous classes of methods. However, there are still a range of topics where additional insight is needed to pave the way for a more widespread application of DRNs in EEG. These include architecture design questions such as network size and end-to-end ability as well as model training questions. How these factors affect model performance has not been explored. Additionally, it is not clear how the data within these networks is transformed, and whether this would correlate with traditional EEG decoding. Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters. Networks were tested on two public EEG datasets and compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the ConvNets, and in doing so use physiologically plausible frequency regions. We also show that the end-to-end approach learns more complex filters than traditional band-pass filters targeting the classical alpha, beta, and gamma frequency bands of the EEG, and that performance can benefit from channel specific filtering approaches. Additionally, architectural analysis revealed areas for further improvement due to the possible loss of Riemannian specific information throughout the network. Our study thus shows how to design and train DRNs to infer task-related information from the raw EEG without the need of handcrafted filterbanks and highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for high-performance EEG decoding.Comment: 26 pages, 15 Figure

New Directions in Subband Coding

Two very different subband coders are described. The first is a modified dynamic bit-allocation-subband coder (D-SBC) designed for variable rate coding situations and easily adaptable to noisy channel environments. It can operate at rates as low as 12 kb/s and still give good quality speech. The second coder is a 16-kb/s waveform coder, based on a combination of subband coding and vector quantization (VQ-SBC). The key feature of this coder is its short coding delay, which makes it suitable for real-time communication networks. The speech quality of both coders has been enhanced by adaptive postfiltering. The coders have been implemented on a single AT&T DSP32 signal processo

From spline wavelet to sampling theory on circulant graphs and beyond– conceiving sparsity in graph signal processing

Graph Signal Processing (GSP), as the field concerned with the extension of classical signal processing concepts to the graph domain, is still at the beginning on the path toward providing a generalized theory of signal processing. As such, this thesis aspires to conceive the theory of sparse representations on graphs by traversing the cornerstones of wavelet and sampling theory on graphs. Beginning with the novel topic of graph spline wavelet theory, we introduce families of spline and e-spline wavelets, and associated filterbanks on circulant graphs, which lever- age an inherent vanishing moment property of circulant graph Laplacian matrices (and their parameterized generalizations), for the reproduction and annihilation of (exponen- tial) polynomial signals. Further, these families are shown to provide a stepping stone to generalized graph wavelet designs with adaptive (annihilation) properties. Circulant graphs, which serve as building blocks, facilitate intuitively equivalent signal processing concepts and operations, such that insights can be leveraged for and extended to more complex scenarios, including arbitrary undirected graphs, time-varying graphs, as well as associated signals with space- and time-variant properties, all the while retaining the focus on inducing sparse representations. Further, we shift from sparsity-inducing to sparsity-leveraging theory and present a novel sampling and graph coarsening framework for (wavelet-)sparse graph signals, inspired by Finite Rate of Innovation (FRI) theory and directly building upon (graph) spline wavelet theory. At its core, the introduced Graph-FRI-framework states that any K-sparse signal residing on the vertices of a circulant graph can be sampled and perfectly reconstructed from its dimensionality-reduced graph spectral representation of minimum size 2K, while the structure of an associated coarsened graph is simultaneously inferred. Extensions to arbitrary graphs can be enforced via suitable approximation schemes. Eventually, gained insights are unified in a graph-based image approximation framework which further leverages graph partitioning and re-labelling techniques for a maximally sparse graph wavelet representation.Open Acces

Three dimensional reconstruction of plant roots via low energy x-ray computed tomography

Plant roots are vital organs for water and nutrient uptake. The structure and spatial distribution of plant roots in the soil affects a plant's physiological functions such as soil-based resource acquisition, yield and its ability to live under abiotic stress. Visualizing and quantifying roots' configuration below the ground can help in identifying the phenotypic traits responsible for a plant's physiological functions. Existing efforts have successfully employed X-ray computed tomography to visualize plant roots in three-dimensions and to quantify their complexity in a non-invasive and non-destructive manner. However, they used expensive and less accessible industrial or medical tomographic systems. This research uses an inexpensive, lab-built X-ray computed tomography (CT) system, operating at lower energy levels (30kV-40kV), to obtain two-dimensional projections of a plant root from different viewpoints. I propose image processing pipelines to segment roots and generate a three-dimensional model of the root system architecture from the two-dimensional projections. Observing that a Gaussian-shaped curve can approximate the cross-sectional intensity profle of a root segment, I propose a novel multi-scale matched filtering with a two-dimensional Gaussian kernel to enhance the root system. The filter assumes different orientations to highlight the root segments grown in different directions. The roots are isolated from the background by manual thresholding, followed by a mathematical morphological process to reduce spurious noise. The segmented images are filtered back projected to generate a three-dimensional model of the plant root system. The results from the research conducted show that the proposed method yields a structurally consistent three-dimensional model of the plant root image set obtained in the air, whereas alternate methods could not process the image set. For plant root images collected in the air, the three-dimensional model generated from the proposed matched-guided filtering and filtered back projection has a better contrast measure (0.0036) compared to the contrast measure (0.099) of the three-dimensional model created from raw images. For plant root images captured in the soil, proposed multiscale matched filtering resulted in better receiver operating characteristic curves than the raw images. Compared to Otsu's thresholding, multi-scale root enhancement and thresholding have reduced the average false positive rate from 0.344 to 0.042, and improved the average F1 score from 0.4 to 0.775. Experimental results show that the proposed root enhancement methods are robust to the number of orientational filters chosen, and are sensitive to the filter length selected. Small size filters are preferred, since increasing the filter length increases the number of false positives around root segments.Includes bibliographical reference