Michele Gasparini

INTRODUCING LEGENDRE NONLINEAR FILTERS

This paper introduces a novel sub-class of linear-in-the-parameters nonlinear filters, the Legendre nonlinear filters. Their basis functions are polynomials, specifically, products of Legendre polynomial expansions of the input signal samples. Legendre nonlinear filters share many of the properties of the recently introduced classes of Fourier nonlinear filters and even mirror Fourier nonlinear filters, which are based on trigonometric basis functions. In fact, Legendre nonlinear filters are universal approximators for causal, time invariant, finite-memory, continuous, nonlinear systems and their basis functions are mutually orthogonal for white uniform input signals. In adaptive applications, gradient descent algorithms with fast convergence speed and efficient nonlinear system identification algorithms can be devised. Experimental results, showing the potentialities of Legendre nonlinear filters in comparison with other linear-in-the-parameters nonlinear filters, are presented and commented.

Identification of Hammerstein model using cubic splines and FIR filtering

Nonlinear models are exploited in the field of digital audio systems for modelling most of real-world devices that show a nonlinear behaviour. Among nonlinear models, Hammerstein systems are realized through a static nonlinearity cascaded with a linear filter. In this paper, the Hammerstein coefficients are estimated using an adaptive Catmull-Rom cubic spline for the static nonlinearity and an adaptive FIR filter for the dynamic linear system also introducing a preprocessing for the time delay estimation. Experimental results confirm the effectiveness of the proposed approach, making also comparisons with existing techniques of the state of the art.

Intelligent Acoustic Interfaces With Multisensor Acquisition for Immersive Reproduction

Immersive speech communication systems have been gaining increasing attention due to their ability to reproduce enhanced acoustic images, and thus achieving good performance in terms of sound quality and accuracy. In this context , a fundamental role is played by intelligent acoustic interfaces (IAIs), which aim at acquiring and/or reproducing desired acoustic information with enhanced perception. The recent widespread availability of multimedia devices, equipped with different kind of sensors, has broadened the range of data processing methods, thus giving a chance for developing advanced IAIs. In this paper, we propose an immersive communication system composed of two IAIs: the first one exploits microphones and cameras, together with a signal processing system, to reduce unwanted noise and enhance the speech quality of the desired information in the transmitting room; the second one is an advanced reproduction system based on a loudspeaker array and on an effective wave field synthesis technique capable of reproducing the spatial perception of the desired speech source in the receiving room. The whole system has been assessed in simulated and real immersive communication scenarios: objective and subjective evaluations have been shown the effectiveness of the proposed system.

Advanced intelligent acoustic interfaces for multichannel audio reproduction

Nowadays, there is a large interest towards multimedia audio systems as a consequence of the development of advanced digital signal processing techniques. In particular, immersive speech communication system has gaining increasing attention since they allow to reproduce realistic acoustic image, and thus achieving good performance in terms of sound quality and accuracy. In this scenario a fundamental role is played by intelligent acoustic interfaces which aim at acquiring audio information, processing it, and returning the processed information to the audio rendering system. In this paper, an effective intelligent acoustic interface composed of a microphone array and a signal processing system capable to enhance the intelligibility of the desired information of the transmitting room is proposed, combined with an advanced reproduction system based on an efficient application of a wave field synthesis technique capable to reproduce an immersive scenario in the receiving room. The whole system has been assessed within a speech communication application involving a moving desired source in a real scenario: objective and subjective evaluation has been reported in order to show the overall system performance.

An adaptive multichannel identification system for room response equalization

This paper deals with an adaptive multichannel and multiple position room response equalization system capable of improving the listening experience in a real environment. An adaptive and accurate estimation of the room responses is provided introducing a normalized least mean square optimization approach with a variable step-size, and taking advantage of a multichannel interchannel coherence reduction technique based on the missing fundamental phenomenon. Then, the equalizer is designed with the introduction of a new prototype function obtained by combining the impulse responses recorded in the real environment to be equalized with quasi-anechoic impulse responses. Several experiments have been performed in a real scenario comparing the proposed approach with the state of the art in terms of objective and subjective measures.

Combining evolution strategies and neural network procedures for compression driver design.

Compression driver design involves the study of complex mathematical models characterized by a great number of variables, implying high computational cost and long design time. Therefore, an optimization procedure is required to enhance the design procedure, especially from the parameters point of view. In this paper, a combined approach based both on evolution strategy procedure and neural network model is presented. Taking into consideration several tests on a real compression driver, the proposed method is capable to enhance the design procedure from the point of view of obtained frequency response and of the computational performance.

An optimization procedure for a nonlinear system identification approach based on cubic splines

Nonlinear system identification is intended to model the nonlinear behavior of real-world devices. Among the state of the art, nonlinear modelling based on the Hammerstein cascade is a well-known and effective technique. The introduction in the model of an adaptive Catmull-Rom cubic spline and an adaptive FIR filter, also including a pre-processing for the system delay estimation, has been discussed in previous work. Therefore, starting from this result, an optimization procedure for the correct parametrization of this solution is presented in this paper based on the minimization of the mean square error chosen as fitness function. Several simulations were carried out in order to prove the effectiveness of the proposed solution in correctly setting the parameters of the adaptive algorithm. Tests were performed on a real electroacoustic device operating at different distortion level conditions. In particular, three main issues were taken into consideration, focusing first, on the validation of the optimization procedure, then, on the analysis of the sensitivity to the spline length, and finally, on the evaluation of the convergence parameters.