Reinhold Orglmeister

The principles of software QRS detection

The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

Blind source separation of real world signals

We present a method to separate and deconvolve sources which have been recorded in real environments. The use of noncausal FIR filters allows us to deal with nonminimum mixing systems. The learning rules can be derived from different viewpoints such as information maximization, maximum likelihood and negentropy which result in similar rules for the weight update. We transform the learning rule into the frequency domain where the convolution and deconvolution property becomes a multiplication and division operation. In particular the FIR polynomial algebra techniques as used by Lambert present an efficient tool to solve true phase inverse systems allowing a simple implementation of noncausal filters. The significance of the methods is shown by the successful separation of two voices and separating a voice that has been recorded with loud music in the background. The recognition rate of an automatic speech recognition system is increased after separating the speech signals.

Blind source separation of nonlinear mixing models

We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms.

Combining time-delayed decorrelation and ICA: towards solving the cocktail party problem

We present methods to separate blindly mixed signals recorded in a room. The learning algorithm is based on the information maximization in a single layer neural network. We focus on the implementation of the learning algorithm and on issues that arise when separating speakers in room recordings. We used an infomax approach in a feedforward neural network implemented in the frequency domain using the polynomial filter matrix algebra technique. A fast convergence speed was achieved by using a time-delayed decorrelation method as a preprocessing step. Under minimum-phase mixing conditions this preprocessing step was sufficient for the separation of signals. These methods successfully separated a recorded voice with music in the background (cocktail party problem). Finally, we discuss problems that arise in real world recordings and their potential solutions.

Nonlinear Postprocessing for Blind Speech Separation

Frequency domain ICA has been used successfully to separate the utterances of interfering speakers in convolutive environments, see e.g. [6],[7]. Improved separation results can be obtained by applying a time frequency mask to the ICA outputs. After using the direction of arrival information for permutation correction, the time frequency mask is obtained with little computational effort. The proposed postprocessing is applied in conjunction with two frequency domain ICA methods and a beamforming algorithm, which increases separation performance for reverberant, as well as for in-car speech recordings, by an average 3.8dB. By combined ICA and time frequency masking, SNR-improvements up to 15dB are obtained in the car environment. Due to its robustness to the environment and regarding the employed ICA algorithm, time frequency masking appears to be a good choice for enhancing the output of convolutive ICA algorithms at a marginal computational cost.

Separation and robust recognition of noisy, convolutive speech mixtures using time-frequency masking and missing data techniques

Time-frequency masking has emerged as a powerful technique for source separation of noisy and convolved speech mixtures. It has also been applied successfully for noisy speech recognition. But while significant SNR gains are possible by adequate masking functions, speech recognition performance suffers from the involved nonlinear operations so that the greatly improved SNR often contrasts with only slight improvements in the recognition rate. To address this problem, marginalization techniques have been used for speech recognition, but they rely on speech recognition and source separation to be carried out in the same domain. However, source separation and denoising are often carried out in the short-time-Fourier-transform (STFT) domain, whereas the most useful speech recognition features are e.g. mel-frequency cepstral coefficients (MFCCs), LPC-cepstral coefficients and VQ-features. In these cases, marginalization techniques are not directly applicable. Here, another approach is suggested, which estimates sufficient statistics for speech features in the preprocessing (e.g. STFT-) domain, propagates these statistics through the transforms from the spectrum to e.g. the MFCCs of a speech recognition system and uses the estimated statistics for missing data speech recognition. With this approach, significant gains can be achieved in speech recognition rates, and in this context, time-frequency masking yields recognition rate improvements of more than 35% when compared to TF-masking based source separation

Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions

When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA) has proven a valuable tool for this purpose. However, ICA outputs can still contain strong residual components of the interfering speakers whenever noise or reverberation is high. In such cases, nonlinear postprocessing can be applied to the ICA outputs, for the purpose of reducing remaining interferences. In order to improve robustness to the artefacts and loss of information caused by this process, recognition can be greatly enhanced by considering the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic. The aim of this paper is to show the potential to improve recognition of multiple overlapping speech signals through nonlinear postprocessing together with uncertainty-based decoding techniques.

Computing MMSE Estimates and Residual Uncertainty Directly in the Feature Domain of ASR using STFT Domain Speech Distortion Models

In this paper we demonstrate how uncertainty propagation allows the computation of minimum mean square error (MMSE) estimates in the feature domain for various feature extraction methods using short-time Fourier transform (STFT) domain distortion models. In addition to this, a measure of estimate reliability is also attained which allows either feature re-estimation or the dynamic compensation of automatic speech recognition (ASR) models. The proposed method transforms the posterior distribution associated to a Wiener filter through the feature extraction using the STFT Uncertainty Propagation formulas. It is also shown that non-linear estimators in the STFT domain like the Ephraim-Malah filters can be seen as special cases of a propagation of the Wiener posterior. The method is illustrated by developing two MMSE-Mel-frequency Cepstral Coefficient (MFCC) estimators and combining them with observation uncertainty techniques. We discuss similarities with other MMSE-MFCC estimators and show how the proposed approach outperforms conventional MMSE estimators in the STFT domain on the AURORA4 robust ASR task.

A contextual blind separation of delayed and convolved sources

We present a new method to tackle the problem of separating mixtures of real sources which have been convolved and time-delayed under real world conditions. To this end, we learn two sets of parameters to unmix the mixtures and to estimate the true density function. The solutions are discussed for feedback and feedforward architectures. Since the quality of separation depends on the modeling of the underlying density we propose different methods to closer approximate the density function using some contest. The proposed density estimation achieves separation of a wider class of sources. Furthermore, we employ the FIR polynomial matrix techniques in the frequency domain to invert a true-phase mixing system. The significance of the new method is demonstrated with the successful separation of two speakers and separation of music and speech recorded with two microphones in a reverberating room.

An Uncertainty Propagation Approach to Robust ASR Using the ETSI Advanced Front-End

In this paper, we show how uncertainty propagation, combined with observation uncertainty techniques, can be applied to a realistic implementation of robust distributed speech recognition (DSR) to improve recognition robustness furthermore, with little increase in computational complexity. Uncertainty propagation, or error propagation, techniques employ a probabilistic description of speech to reflect the information lost during speech enhancement or source separation in the time or frequency domain. This uncertain description is then propagated through the feature extraction process to the domain of features used in speech recognition. In this domain, the statistical information can be combined with the statistical parameters of the recognition model by employing observation uncertainty techniques. We show that the combination of a piecewise uncertainty propagation scheme with front-end uncertainty decoding or modified imputation improves the baseline of the advanced front-end (AFE), the state of the art algorithm of the European Telecommunications Standards Institute (ETSI), on the AURORA5 database. We compare this method with other observation uncertainty techniques and show how the use of uncertainty propagation reduces the word error rates without the need for any kind of adaptation to noise using stereo data or iterative parameter estimation.