New domain-specific languages for creating music and audio applications have typically been created in response to some technological challenge. Recent research has begun looking at how these languages impact our creative and aesthetic choices in music-making but we have little understanding on their effect on our wider programming practice. We present a survey that seeks to uncover what programming practices exist among interactive audio software developers and discover it is highly multi-practice, with developers adopting both exploratory programming and software engineering practice. A Q methodological study reveals that this multi-practice development is supported by different combinations of language features.

Diagnosis of melanoma is fraught with uncertainty, and discordance rates among physicians remain high because of the lack of a definitive criterion. Motivated by this challenge, this paper first introduces the Patch Weyl transform (PWT), a 2-dimensional variant of the Weyl transform. It then presents a method for classifying pump-probe images of melanocytic lesions based on the PWT coefficients. Performance of the PWT coefficients is shown to be superior to classification based on baseline intensity, on standard descriptors such as the Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), and on coefficients derived from PCA and Fourier representations of the data.

We study the network-wide neighbour discovery problem in wireless networks in which each node in a network must discovery the network interface addresses (NIAs) of its neighbour. We work within the rapid on-off division duplex framework proposed by Guo and Zhang in [5] in which all nodes are assigned different on-off signatures which allow them listen to the transmissions of neighbouring nodes during their off slots; this leads to a compressed sensing problem at each node with a collapsed codebook determined by a given node's transmission signature. We propose sparse Kerdock matrices as codebooks for the neighbour discovery problem. These matrices share the same row space as certain Delsarte-Goethals frames based upon Reed Muller codes, whilst at the same time being extremely sparse. We present numerical experiments using two different compressed sensing recovery algorithms, One Step Thresholding (OST) and Normalised Iterative Hard Thresholding (NIHT). For both algorithms, a higher proportion of neighbours are successfully identified using sparse Kerdock matrices compared to codebooks based on Reed Muller codes with random erasures as proposed in [13]. We argue that the improvement is due to the better interference cancellation properties of sparse Kerdock matrices when collapsed according to a given node's transmission signature. We show by explicit calculation that the coherence of the collapsed codebooks resulting from sparse Kerdock matrices remains near-optimal.

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.

A novel algorithm for computing the Walsh-Hadamard transform (WHT) is proposed, which consists entirely of Haar wavelet transforms. It is proved that the algorithm, referred to as the cascading Haar wavelet algorithm, shares precisely the same serial complexity as the popular divide-and-conquer algorithm for the WHT. A natural parallelization of the algorithm is also proposed, which has a number of attractive features.

As shown by Blumensath and Davies (2009) and Baraniuk et al. (2010), signals whose wavelet coefficients exhibit a rooted tree structure can be recovered using specially adapted compressed sensing algorithms from just n = O(k) measurements, where k is the sparsity of the signal. Motivated by these results, we introduce a simplified proportional-dimensional asymptotic framework, which enables the quantitative evaluation of recovery guarantees for tree-based compressed sensing. In the context of Gaussian matrices, we apply this framework to existing worst-case analysis of the iterative tree projection (ITP) algorithm, which makes use of the tree-based restricted isometry property (RIP). Within the same framework, we then obtain quantitative results based on a new method of analysis, which considers the fixed points of the algorithm. By exploiting the realistic average-case assumption that the measurements are statistically independent of the signal, we obtain significant quantitative improvements when compared with the treebased RIP analysis. Our results have a refreshingly simple interpretation, explicitly determining a bound on the number of measurements that are required as a multiple of the sparsity. For example, we prove that exact recovery of binary tree-based signals from noiseless Gaussian measurements is asymptotically guaranteed for ITP with constant stepsize provided n ≥ 50k. All our results extend to the more realistic case in which measurements are corrupted by noise.

In this paper, we apply the Weyl transform to represent the vocalization of marine mammals. In contrast to other popular representation methods, such as the MFCC and the Chirplet transform, the Weyl transform captures the global information of signals. This is especially useful when the signal has low order polynomial phase. We can reconstruct the signal from the coefficients obtained from the Weyl transform, and perform classification based on these coefficients. Experimental results show that classification using features extracted from the Weyl transform outperforms the MFCC and the Chirplet transform on our collected whales data.

We present a new recovery analysis for a standard compressed sensing algorithm, Iterative Hard Thresholding (IHT) (Blumensath and Davies, 2008), which considers the fixed points of the algorithm. In the context of arbitrary measurement matrices, we derive a sufficient condition for the convergence of IHT to a fixed point and a necessary condition for the existence of fixed points. These conditions allow us to perform a sparse signal recovery analysis in the deterministic noiseless case by implying that the original sparse signal is the unique fixed point and limit point of IHT, and in the case of Gaussian measurement matrices and noise by generating a bound on the approximation error of the IHT limit as a multiple of the noise level. By generalizing the notion of fixed points, we extend our analysis to the variable stepsize Normalised IHT (Blumensath and Davies, 2010). For both stepsize schemes, we obtain lower bounds on asymptotic phase transitions in a proportional-dimensional framework, quantifying the sparsity/undersampling tradeoff for which recovery is guaranteed. Exploiting the reasonable average-case assumption that the underlying signal and measurement matrix are independent, comparison with previous results within this framework shows a substantial quantitative improvement.

We propose a dynamic programming algorithm for projection onto wavelet tree structures. In contrast to other recently proposed algorithms which only give approximate tree projections for a given sparsity, our algorithm is guaranteed to calculate the projection exactly. We also prove that our algorithm has O(Nk) complexity, where N is the signal dimension and k is the sparsity of the tree approximation.