Transparent communication

Transparent communication refers to the audio signal processing which is applied in communication applications The goal is to make the audio as transparent as possible in the sense that the reproduced audio should ideally be free from reverberation noise acoustical echos and mixed speakers Application areas are for example teleconferencing and handsfree telephony This paper presents new ideas for the implementation of such a system In particular the use of blind signal separation is examined and new ideas are presented for the joint implementation of the MultiChannel Acoustical Echo Canceler MC AEC and the Blind Signal Separation BSS In this way acoustical quality can be improved at a reduced computational cost

On the relationship between uniform and recurrent nonuniform discrete-time sampling schemes

Recurrent nonuniform discrete-time signal samples can be regarded as a combination of K mutual delayed sequences of uniform discrete-time signal samples taken at one Kth of the Nyquist sampling rate. This paper introduces a new alternative discrete-time analysis model of the recurrent nonuniform sampling scenario. This model can be described by the analysis part of a uniform discrete Fourier transform (DFT) modulated filterbank from which the K uniformly distributed and down sampled frequency bands are mixed in a very specific way. This description gives a clear relationship between uniform and recurrent nonuniform discrete-time sampling schemes. A side benefit of this model is an efficient structure with which one can reconstruct uniform discrete-time Nyquist signal samples from recurrent nonuniform samples with known mutual delays between the nonuniform distributed samples. This reconstruction structure can be viewed as a natural extension of the synthesis part of an uniform DFT modulated filterbank

A new algorithm for joint blind signal separation and acoustic echo canceling

The problem of joint blind signal separation and acoustic echo cancelling arises in applications such as teleconferencing and voice controlled machinery. Microphones pick up a signal of the desired speaker together with contributions of other speakers and loudspeakers in these applications. The contributions of these loudspeaker signals to the microphone signals need to be cancelled. The remaining signals are then separated so that the individual local speakers are recovered. In this paper an extension of the recently introduced Convolutive Blind Signal Separation algorithm; CoBliSS is presented. This extended algorithm is capable of performing combined blind signal separation and acoustical echo cancelling at a low computational cost. The performance of the extended CoBliSS algorithm is evaluated using audio that is recorded in a real acoustical environment

On the indeterminacies of convolutive blind signal separation based on second-order statistics

Recently, several blind signal separation algorithms have been developed which are based on second order statistics. Little has been published however on whether second order statistics are sufficient to obtain a unique solution. Especially for applications that involve convolutive mixing and unmixing of signals that are correlated in time, there is a lack of knowledge on why and in what cases second order statistics suffice. This paper investigates the indeterminacies that are introduced when second order statistics are used and presents a theorem for the unmixing system to be uniquely found using second order statistics

On the performance of too short adaptive FIR filters

Performance analyses of adaptive algorithms such as LMS and RLS often rely on the assumption that the input signal is stationary. Also it often is assumed that the adaptive nite impulse response FIR filter is long enough to make a good approximation of the unknown system. In many practical situations these assumptions do not hold and the instantaneous misadjustment of the adaptive filter can grow large. In this paper this effect is investigated and two methods are presented to improve the performance of the adaptive filter

Realtime realization aspects of the CoBliSS blind signal separation algorithm

Recently, the Convolutive Blind Signal Separation algorithm (CoBliSS) was introduced. CoBliSS is based on second order statics only and is able to control a multichannel filter with thousands of tabs as is required in acoustical applications. In this paper the feasibility of a real-time implementation of the CoBliSS algorithm is investigated. An efficient implementation is proposed and the corresponding computational complexity is discussed

WDM monitoring technique using adaptive blind signal separation

We present a cost-effective method of monitoring the performance of wavelength-division-multiplexed (WDM) channels. The method is based on simple optical signal processing in combination with electronic signal processing. The photocurrent of a detected (multi-channel) optical signal is analysed using an adaptive blind signal separation method. A maximum data decorrelation criterion is used to separate the WDM channels. We show experimentally that four WDM channels can be reconstructed accurately by this numerical method

Ultrasonic array doppler sensing for human movement classification

Classification of human movements is an important problem in healthcare and well-being applications. An ultrasonic array Doppler sensing method is proposed for classifying movements from a given set. The proposed method uses velocity and angular information derived from Doppler frequencies and direction-of-arrival (DoA) by processing the signals at the receiver sensor array. Doppler frequency estimation is done by obtaining an initial estimate based on the Fourier transform in conjunction with a predictive tracker. A Root-MUSIC algorithm is used at the estimated Doppler frequencies to obtain DoA corresponding to the dominating moving object. Using speed, direction, and angle as features, a Bayesian classifier is employed to distinguish between a set of movements. The performance of the proposed method is evaluated using an analytical model of arm movements and also using experimental data sets. The proposed ultrasonic Doppler array sensor and processing methods provide a new, compact solution to human arm movement classification

A frequency domain blind signal separation method based on decorrelation

This paper addresses the issue of separating multiple speakers from mixtures of these that are obtained using multiple microphones in a room. An adaptive blind signal separation algorithm, which is entirely based on second-order statistics, is derived. One of the advantages of this algorithm is that no parameters need to be tuned. Moreover, an extension of the algorithm that can simultaneously deal with blind signal separation and echo cancellation is derived. Experiments with real recordings have been carried out, showing the effectiveness of the algorithm for real-world signal