Søren Holdt Jensen

Joint DOA and multi-pitch estimation based on subspace techniques

In this article, we present a novel method for high-resolution joint direction-of-arrivals (DOA) and multi-pitch estimation based on subspaces decomposed from a spatio-temporal data model. The resulting estimator is termed multi-channel harmonic MUSIC (MC-HMUSIC). It is capable of resolving sources under adverse conditions, unlike traditional methods, for example when multiple sources are impinging on the array from approximately the same angle or similar pitches. The effectiveness of the method is demonstrated on a simulated an-echoic array recordings with source signals from real recorded speech and clarinet. Furthermore, statistical evaluation with synthetic signals shows the increased robustness in DOA and fundamental frequency estimation, as compared with to a state-of-the-art reference method.

Reduction of broad-band noise in speech by truncated QSVD

We consider an algorithm for reduction of broadband noise in speech based on signal subspaces. The algorithm is formulated by means of the quotient singular value decomposition (QSVD). With this formulation, a prewhitening operation becomes an integral part of the algorithm. We demonstrate that this is essential in connection with updating issues in real-time recursive applications. We also illustrate by examples that we are able to achieve a satisfactory quality of the reconstructed signal.

Prediction of future fading based on past measurements

The possibility of predicting the complex radio channel ahead in time when the terminal is moving in a straight line is investigated. It is assumed that a number of samples with constant spatial sampling period are available. The Doppler spectrum and the amplitude of the complex scatterers are determined using an ESPRIT-type algorithm, and the signals are then extrapolated into the future assuming that the scatterers remain constant. Synthetic and real data both indicate that for a large number of scatterers, a continuous Doppler spectrum, the signal may be predicted about a wavelength ahead. This assumes that the sampling is sufficiently dense.

Algorithms and software for total variation image reconstruction via first-order methods

This paper describes new algorithms and related software for total variation (TV) image reconstruction, more specifically: denoising, inpainting, and deblurring. The algorithms are based on one of Nesterov’s first-order methods, tailored to the image processing applications in such a way that, except for the mandatory regularization parameter, the user needs not specify any parameters in the algorithms. The software is written in C with interface to Matlab (version 7.5 or later), and we demonstrate its performance and use with examples.

Theorems on Positive Data: On the Uniqueness of NMF

We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not. The theorems are illustrated by several examples showing the use of the theorems and their limitations. We have shown that corruption of a unique NMF matrix by additive noise leads to a noisy estimation of the noise-free unique solution. Finally, we use a stochastic view of NMF to analyze which characterization of the underlying model will result in an NMF with small estimation errors.

A perceptual model for sinusoidal audio coding based on spectral integration

Psychoacoustical models have been used extensively within audio coding applications over the past decades. Recently, parametric coding techniques have been applied to general audio and this has created the need for a psychoacoustical model that is specifically suited for sinusoidal modelling of audio signals. In this paper, we present a new perceptual model that predicts masked thresholds for sinusoidal distortions. The model relies on signal detection theory and incorporates more recent insights about spectral and temporal integration in auditory masking. As a consequence, the model is able to predict the distortion detectability. In fact, the distortion detectability defines a (perceptually relevant) norm on the underlying signal space which is beneficial for optimisation algorithms such as rate-distortion optimisation or linear predictive coding. We evaluate the merits of the model by combining it with a sinusoidal extraction method and compare the results with those obtained with the ISO MPEG-1 Layer I-II recommended model. Listening tests show a clear preference for the new model. More specifically, the model presented here leads to a reduction of more than 20% in terms of number of sinusoids needed to represent signals at a given quality level.

Joint High-Resolution Fundamental Frequency and Order Estimation

In this paper, we present a novel method for joint estimation of the fundamental frequency and order of a set of harmonically related sinusoids based on the multiple signal classification (MUSIC) estimation criterion. The presented method, termed HMUSIC, is shown to have an efficient implementation using fast Fourier transforms (FFTs). Furthermore, refined estimates can be obtained using a gradient-based method. Illustrative examples of the application of the algorithm to real-life speech and audio signals are given, and the statistical performance of the estimator is evaluated using synthetic signals, demonstrating its good statistical properties.

Sparse Linear Prediction and Its Applications to Speech Processing

The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the linear prediction framework. These tools have shown to be effective in several issues related to modeling and coding of speech signals. For speech analysis, we provide predictors that are accurate in modeling the speech production process and overcome problems related to traditional linear prediction. In particular, the predictors obtained offer a more effective decoupling of the vocal tract transfer function and its underlying excitation, making it a very efficient method for the analysis of voiced speech. For speech coding, we provide predictors that shape the residual according to the characteristics of the sparse encoding techniques resulting in more straightforward coding strategies. Furthermore, encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application to sparse linear predictive coding. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Extensive experimental results are provided to support the effectiveness of the proposed methods, showing the improvements over traditional linear prediction in both speech analysis and coding.

Experimental investigation of multipath richness for multi-element transmit and receive antenna arrays

The multi-element antenna arrays concept with M elements at the mobile station (MS) in combination with N elements at the base station (BS) is experimentally investigated. Forschini (1996) has shown very promising results to improve the spectral efficiency in a rich scattering environment. The performance of the M/spl times/N concept is evaluated in terms of the number of independent parallel channels, diversity gain and total capacity in an outdoor to indoor microcellular environment. It is shown that the eigenanalysis provides a tool to describe the effective number of parallel channels in a multi-element array configuration. Practical results on spectral efficiency are presented for different antenna setups applied to different propagation scenarios. Also it is shown that polarization diversity is an attractive solution to achieve decorrelated antenna elements and subsequently provide a more robust system in terms of spectral efficiency within the microcell. Results show that a total capacity of 27.9 b/s/Hz can be achieved for an uncorrelated propagation environment and 17 b/s/Hz for a correlated one with a mean signal to noise ratio (SNR) of 30 dB in the case of a 4/spl times/4 antenna set-up.

FIR filter representations of reduced-rank noise reduction

We show that the reduced-rank output signal computed via truncated (Q)SVD is identical to that from an array of parallelly connected analysis-synthesis finite impulse response (FIR) filter pairs. The filter coefficients are determined by the (Q)SVD, and the filters provide an explicit description of the reduced-rank noise reduction algorithm in the frequency domain.