Enrique Masgrau

Novel FxLMS Convergence Condition With Deterministic Reference

A novel analysis of FxLMS convergence when the reference signal is deterministic is presented in this paper. The simple case of a sinusoidal reference is considered first, to be later extended to any combination of multiple sinusoids. In both cases, we derive an upper bound for the algorithm step size which ensures convergence. In the derivation of this result there is no need of any of the usual approximations, such as independence between reference and weights or slow convergence, which are not suitable for deterministic references. Instead, we consider the common cases where the adaptive system shows linear time-invariant behavior. The upper bound obtained for the step size is in good agreement with empirical measurements

Robust continuous speech recognition system based on a microphone array

A robust speech recognition system for videoconference applications is presented based on a microphone array. By means of a microphone array, the speech recognition system is able to know the position of the users and increase the signal-to-noise ratio (SNR) between the desired speaker signal and the interference from the other users. The user positions are estimated by means of the combination of a direction of arrival (DOA) estimation method with a speaker identification system. The beamforming is performed by using the spatial references of the desired speaker and the interference locations. A minimum variance algorithm with spatial constraints working in the frequency domain is used to design the weights of the broadband microphone array. Results of the speech recognition system are reported in a simulated environment with several users asking questions to a geographic data base.

Simplified gradient calculation in adaptive IIR lattice filters

A simplified algorithm is presented for computing the gradient in adaptive IIR (infinite impulse response) lattice filters. For a filter with N zeros and N poles, this algorithm requires only order N computations. Several computer simulations were performed to compare the performance of the proposed O(N) formulation and the conventional O(N/sup 2/) formulation, and no difference in their behavior was found. In these simulations the full Hessian and the diagonal Hessian adaptive algorithms were used in a system-identification configuration. It is concluded that the proposed method allows the development of robust adaptive IIR algorithms with a cost proportional to the filter order. >

The adaptive linear combiner with a periodic-impulse reference input as a linear comb filter

Abstract In this paper we present a particularization of the adaptive linear combiner (ALC) filter structure, that results in a linear time-invariant comb filter suitable for the estimation of periodic signals and repetitive time-locked signals. The ALC is used in its transversal form, and the reference input is a periodic unitary-impulse train signal. When the LMS algorithm is used we show that the structure results in a simple and efficient linear time-invariant comb filter, taking as output the output of the ALC. This comb filter has lobe widths proportional to the μ gain parameter of the LMS algorithm, and the separation between lobes is controlled by the period L of the periodic impulse train. We have also analyzed this filter when applied to estimate repetitive signals time-locked to a stimulus, and we show that the effect of a temporal misalignment in the determination of the stimulus results in a low-pass filtering effect, with cut-off frequency inversely proportional to the dispersion of the impulse estimation. This effect is specially important when the time occurrence of the stimulus is not directly accessible and needs to be estimated from some ‘noise affected’ procedures, as in Electrocardiographic signals. The filter is also shown to be equivalent to a time-sequenced adaptive filter with one weight. Finally, an application to somatosensory evoked potentials estimation is presented.

Speech reinforcement system for car cabin communications

A speech reinforcement system is presented to improve communication between the front and the rear passengers in large motor vehicles. This type of communication can be difficult due to a number of factors, including distance between speakers, noise and lack of visual contact. The system described makes use of a set of microphones to pick up the speech of each passenger, then it amplifies these signals and plays them back to the cabin through the car audio loudspeaker system. The two main problems are noise amplification and electro-acoustic coupling between loudspeakers and microphones. To overcome these problems the system uses a set of acoustic echo cancellers, echo suppression filters and noise reduction stages. In this paper, the stability of a speech reinforcement system is studied. We propose a solution based on echo cancellers and residual echo suppression filters. The spectral estimation method for the power spectral density of the residual echo existing after the echo canceller is presented along with the derivation of the optimal residual echo suppression filter. Some results about the performance of the proposed system are also provided.

Cabin car communication system to improve communications inside a car

This paper presents a cabin car communication system (CCCS) to improve the communication among passengers inside a car. Noise, distance between speakers and many other factors make difficult to maintain a conversation inside a car. The CCCS picks up the speech of each passenger, amplifies it, and uses the car loudspeaker system to return it into the cabin. Two problems arise when designing a CCCS; the electro-acoustic coupling between loudspeakers and microphones, and the amplification of the inside car noise. As a result of the first problem, the system may become unstable. To maintain the stability of the system, the CCCS makes use of a robust acoustic echo cancellation scheme based on system identification and a Wiener echo suppressor. Using a noise reduction system based on Wiener filtering reduces second problem, noise amplification. Experimental results showing the performance of the system in terms of acoustic echo and noise reduction and speech reinforce are presented. A system with 2-input/2-output channels has been built on a DSP board for medium size cars and minivan vehicles.

Sampling in-phase and quadrature components of band-pass signals

A filtering method for sampling the lowpass equivalent of a bandpass analog signal is introduced. This input is sampled at exactly twice the sampling rate of the in-phase and quadrature components provided at the output. The sampling frequency determination and the lowpass decimator filter design are straightforward. The practical implementation of the sampling method is accomplished by the polyphase realization of the filter; this implementation is particularly suitable for linear phase FIR designs.

Step size bound of the sequential partial update LMS algorithm with periodic input signals

This paper derives an upper bound for the step size of the sequential partial update (PU) LMS adaptive algorithm when the input signal is a periodic reference consisting of several harmonics. The maximum step size is expressed in terms of the gain in step size of the PU algorithm, defined as the ratio between the upper bounds that ensure convergence in the following two cases: firstly, when only a subset of the weights of the filter is updated during every iteration; and secondly, when the whole filter is updated at every cycle. Thus, this gain in step-size determines the factor by which the step size parameter can be increased in order to compensate the inherently slower convergence rate of the sequential PU adaptive algorithm. The theoretical analysis of the strategy developed in this paper excludes the use of certain frequencies corresponding to notches that appear in the gain in step size. This strategy has been successfully applied in the active control of periodic disturbances consisting of several harmonics, so as to reduce the computational complexity of the control system without either slowing down the convergence rate or increasing the residual error. Simulated and experimental results confirm the expected behavior.

OISTI (an Oral-Interface System to provide Tourist-Information inside a car)

We present a positioning system implemented in Java that is designed to provide tourist information for car users. Most of this multimedia information is stored in a local database in the car, but it is possible to increase or update it by accessing a remote database by mobile telephone. The system can be controlled like any standard computer by keyboard or by mouse, but also offers hands-free control based on an oral interface module, which is very important for a system designed to be used inside a car.

Bounds on Capacity Over Gaussian MIMO Multiaccess Channels with Channel State Information Mismatch

In this paper, we deal with the capacity of Gaussian MEMO multi-access fading channels when the channel knowledge available at the receiver and the transmitter is erroneous. First, upper and lower bounds on the multiaccess capacity region are presented when the channel state information is estimated by means of a MMSE criterion, and consequently, there exist some uncertainty on the channel knowledge. Then, the difference between these bounds for the joint mutual information is analyzed. A final expression for the ergodic sum-capacity gap is obtained as a function of the number of users, the quality of the channel estimates and the number of receive and transmit antennas. Computer results show that adding more users or transmit antennas reduces this gap in equal factor at high SNR