Evaluation of Computing Symmetrical Zolotarev Polynomials of the First Kind

This report summarize and compares with each other various methods for computing the symmetrical Zolotarev Polynomial of the first kind and its spectrum. Suitable criteria are suggested for the comparison. The best numerical stability shows the method employing Chebyshev polynomial recurrence. In case of the polynomial spectrum computation the best method is the one using the difference backward recursion introduced by M. Vlcek. Both methods are able to generate the polynomial of high degree up to, at least, 2000, using 32-bit IEEE floating point arithmetics

Cross-Language Experiment

The contribution addresses the cross-language experiment. The aimwas to test the possibility of the conversion French phoneme modelsinto Czech ones. This model conversion uses the Hidden Markov Models(HMM) classification procedure. The first step consists of theiterative mapping of French models to Czech ones. The mapping is givenby the analysis the confusion matrix. The second step is the Baum-Welchre-estimation resulting in the final models for Czech language. Despiteof the differences between French and Czech languages the finalrecognition score reaches 64% for the phoneme recognition and 74% fordigit recognition. Relatively low recognition accuracy is caused by theinadequate noise model. The experiences gained with the cross-languageexperiment were utilized for the classification of simple human bodymovements. The solution of this problem and results are described inthe second part of this contribution under the title EEG SignalsClassification-Introduction to the Problem

Exploiting Temporal Context in High-Resolution Movement-Related EEG Classification

The contribution presents an application of a movement-related EEG temporal development classification which improves the classification score of voluntary movements controlled by closely localized regions of the brain. A dynamic Hidden Markov Model-based (HMM) classifier specifically designed to capture EEG temporal behavior was used. Surprisingly, HMM classifiers are rarely used for BCI design despite of their advantages. Because of this we also experimented with Learning Vector Quantization, Perceptron, and Support Vector Machine classifiers using a feature space which captures the temporal dynamics of the data. The results presented in this work show that HMM achieves the best performance due to an a priori information on physiological behavior of EEG inserted to the HMM classifier. Feature extraction process and problems with classification were analyzed as well. Classification scores of 66.7% – 94.7% were achieved in our experiments

A Family of Coherence-Based Multi-Microphone Speech Enhancement Systems

This contribution addresses the problem of additive noise reductionin speech picked up by a microphone in a noisy environment. Two systemsbelonging to the family of coherence-based noise cancellers arepresented. Suggested systems have the modular structure using 2 or 4microphones and suppress non-stationary noises in the range of 4 to 17dB depending on the chosen structure and noise characteristics. Thecommon properties are acceptable noise suppression, low speechdistortion and residual noise

ACE Strategy with Virtual Channels

Cochlear implant is an electronic device, which can mediate hearing sensations to profoundly deaf people. Contemporary cochlear implants are sophisticated electronic devices; however, their performance could still be improved. This paper describes an experiment we made in that direction: additional 21 virtual channels were implemented by sequential stimulation of adjacent intracochlear electrodes, and the ACE strategy with virtual channels (ACEv, Advanced Combination Encoder strategy with virtual channels) for the Nucleus® 24 Cochlear Implant System was created and verified in a clinical test with four patients

An Embedded Implementation of Discrete Zolotarev Transform Using Hardware-Software Codesign

The Discrete Zolotarev Transform (DZT) brings an improvement in the field of spectral analysis of non-stationary signals. However, the transformation algorithm called Approximated Discrete Zolotarev Transform (ADZT) suffers from high computational complexity. The Short Time ADZT (STADZT) requires high segment length, 512 samples, and more, while high segment overlap to prevent information loss, 75 % at least. The STADZT requirements along with the ADZT algorithm computational complexity result in a rather high computational load. The algorithm computational complexity, behavior, and quantization error impacts are analyzed. We present a solution which deals with high computational load employing co-design methods targeting Field Programmable Gate Array (FPGA). The system is able to compute one-shot DZT spectrum 2 048 samples long in ≈ 22ms. Real-time STADZT spectrum of a mono audio signal of 16 kHz sampling frequency can be computed with overlap of 91 %

Derivation of Criterion Suitable for Evaluation of Multichannel Noise Reduction Systems for Speech Processing

This paper deals with the theoretical derivation of the Noise Reduction criterion suitable for evaluation of multichannel noise reduction system performance. This criterion is suitable for noise suppression assessment and thus serves as an important step in the development of noise reduction systems. Noise reduction is evaluated in dependence on spatial coherence. The derivations are made for five basic multichannel systems, Delay and sum beamformer, Beamformer with adaptive postprocessing, Generalized sidelobe canceller, Linearly constrained beamformer, and Modified coherence filter

Study of 2-input 2-output Blind Signal Separation by Output Decorrelation

The simulations and experiments representing the initial study of the output decorrelation approach to blind signal separation are presented in this paper. The definition of performance indexes for the evaluation and comparison of different algorithms are proposed. Two algorithms are compared. Some first results of real experiments are discussed

CAR2 - Czech Database of Car Speech

This paper presents new Czech language two-channel (stereo) speech database recorded in car environment. The created database was designed for experiments with speech enhancement for communication purposes and for the study and the design of a robust speech recognition systems. Tools for automated phoneme labelling based on Baum-Welch re-estimation were realised. The noise analysis of the car background environment was done