Mixing matrix estimation using discriminative clustering for blind source separation

Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms.     

Optimality and stability of the K-hyperline clustering algorithm

K-hyperline clustering is an iterative algorithm based on singular value decomposition and it has been successfully used in sparse component analysis. In this paper, we prove that the algorithm converges to a locally optimal solution for a given set of training data, based on Lloyd’s optimality conditions. Furthermore, the local optimality is shown by developing an Expectation-Maximization procedure for learning dictionaries to be used in sparse representations and by deriving the clustering algorithm as its special case. The cluster centroids obtained from the algorithm are proved to tessellate the space into convex Voronoi regions. The stability of clustering is shown by posing the problem as an empirical risk minimization procedure over a function class. It is proved that, under certain conditions, the cluster centroids learned from two sets of i.i.d. training samples drawn from the same probability space become arbitrarily close to each other, as the number of training samples increase asymptotically.     

A software tool for introducing speech coding fundamentals in a DSPcourse

An educational software tool on speech coding is presented. Portions of this program are used in a senior-level DSP (digital signal processing) class at Arizona State University, USA, to expose undergraduate students to speech coding and present speech analysis/synthesis as an application paradigm for many DSP fundamental concepts. The simulation software provides an interactive environment that allows users to investigate and understand speech coding algorithms for a variety of input speech records. Time- and frequency-domain representations of input and reconstructed speech can be graphically displayed and played back on a PC equipped with a standard 16-bit sound card. The program has been developed for use in the MATLAB environment and includes implementations of the FS-1015 LPC-10e, the FS-1016 CELP, the ETSI GSM, the IS-54 VSELP, the G.721 ADPCM, and the G.728 LD-CELP speech coding algorithms, integrated under a common graphical interface     

Interactive online undergraduate laboratories using J-DSP

An interactive Web-based simulation tool called Java-DSP (J-DSP) for use in digital signal processing (DSP)-related electrical engineering courses is described. J-DSP is an object-oriented simulation environment that enables students and distance learners to perform online signal processing simulations, visualize Web-based interactive demos, and perform computer laboratories from remote locations. J-DSP is accompanied by a series of hands-on laboratory exercises that complement classroom and textbook content. The laboratories cover several fundamental concepts, including z transforms, digital filter design, spectral analysis, multirate signal processing, and statistical signal processing. Online assessment instruments for the evaluation of the J-DSP software and the associated laboratory exercises have been developed. Pre/postassessment data have been collected and analyzed for each laboratory in an effort to assess the impact of the tool on student learning.     

Teach-Ware: DSP education resources

This paper describes a variety of TeachWare, i.e., digital signal processing (DSP) education resources that are useful for introducing DSP concepts, operations, and algorithms. The paper provides such information as the type of resource, its status, and format.     

Block time and frequency domain modified covariance algorithms forspectral analysis

Block modified covariance algorithms are proposed for autoregressive parametric spectral estimation. First, the authors develop the block modified covariance algorithm (BMCA) which can be implemented either in the time or in the frequency domain-with the latter being more efficient in high-order cases. A block algorithm is also developed for the energy weighted combined forward and backward prediction. This algorithm is called energy weighted BMCA (EWBMCA) and its performance is analogous to that of the weighted covariance method proposed by Nikias and Scott (1983). Time-varying convergence factors, designed to minimize the error energy from one iteration to the next, are given for both algorithms. In addition, three updating schemes are presented, namely block-by-block, sample-by-sample, and sample-by-sample with time-scale separation. The performance of the proposed algorithms is examined with stationary and nonstationary narrowband and broadband processes, and also with sinusoids in noise. Lastly, the authors discuss the computational complexity of the proposed algorithms and give performance comparisons to existing modified covariance algorithms     

Segmentation, Indexing, and Retrieval for Environmental and Natural Sounds

We propose a method for characterizing sound activity in fixed spaces through segmentation, indexing, and retrieval of continuous audio recordings. Regarding segmentation, we present a dynamic Bayesian network (DBN) that jointly infers onsets and end times of the most prominent sound events in the space, along with an extension of the algorithm for covering large spaces with distributed microphone arrays. Each segmented sound event is indexed with a hidden Markov model (HMM) that models the distribution of example-based queries that a user would employ to retrieve the event (or similar events). In order to increase the efficiency of the retrieval search, we recursively apply a modified spectral clustering algorithm to group similar sound events based on the distance between their corresponding HMMs. We then conduct a formal user study to obtain the relevancy decisions necessary for evaluation of our retrieval algorithm on both automatically and manually segmented sound clips. Furthermore, our segmentation and retrieval algorithms are shown to be effective in both quiet indoor and noisy outdoor recording conditions.      

Transform methods for seismic data compression

The authors consider the development and evaluation of transform coding algorithms for the storage of seismic signals. Transform coding algorithms are developed using the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the Walsh-Hadamard transform (WHT), and the Karhunen-Loeve transform (KLT). These are evaluated and compared to a linear predictive coding algorithm for data rates ranging from 150 to 550 bit/s. The results reveal that sinusoidal transforms are well-suited for robust, low-rate seismic signal representation. In particular, it is shown that a DCT coding scheme reproduces faithfully the seismic waveform at approximately one-third of the original rate     

Smart-antenna systems for mobile communication networks. Part 1. Overview and antenna design

This paper focuses on the interaction and integration of several critical components of a mobile communication network using smart-antenna systems. This wireless network is composed of communicating nodes that are mobile, and its topology is continuously changing. One of the central motivations for this work comes from the observed dependence of the overall network throughput on the design of the adaptive antenna system and its underlying signal processing algorithms. Part 1 of this two-part paper gives a brief overview of smart-antenna systems, including the different types of smart-antenna systems, and the reason for their having gained popularity. Moreover, details of typical antenna array designs suitable for the wireless communication devices are included in this part.