Giorgio Biagetti

Multicomponent AM–FM Representations: An Asymptotically Exact Approach

This paper presents, on the basis of a rigorous mathematical formulation, a multicomponent sinusoidal model that allows an asymptotically exact reconstruction of nonstationary speech signals, regardless of their duration and without any limitation in the modeling of voiced, unvoiced, and transitional segments. The proposed approach is based on the application of the Hilbert transform to obtain an amplitude signal from which an AM component is extracted by filtering, so that the residue can then be iteratively processed in the same way. This technique permits a multicomponent AM-FM model to be derived in which the number of components (iterations) may be arbitrarily chosen. Additionally, the instantaneous frequencies of these components can be calculated with a given accuracy by segmentation of the phase signals. The validity of the proposed approach has been proven by some applications to both synthetic signals and natural speech. Several comparisons show how this approach almost always has a higher performance than that obtained by current best practices, and does not need the complex filter optimizations required by other techniques

Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition

Sport, fitness, as well as rehabilitation activities, often require the accomplishment of repetitive movements. The correctness of the exercises is often related to the capability of maintaining the required cadence and muscular force. Failure to maintain the required force, also known as muscle fatigue, is accompanied by a shift in the spectral content of the surface electromyography (EMG) signal toward lower frequencies. This paper presents a novel approach for simultaneously obtaining exercise repetition frequency and evaluating muscular fatigue, as functions of time, by only using the EMG signal. The mean frequency of the amplitude spectrum (MFA) of the EMG signal, considered as a function of time, is directly related to the dynamics of the movement performed and to the fatigue of the involved muscles. If the movement is cyclic, MFA will display the same pattern and its average will tend to decrease. These two effects have been simultaneously modeled by a two-component AM-FM model based on the Hilbert transform. The method was tested on signals recorded using a wireless system applied to healthy subjects performing dumbbell biceps curls, dumbbell lateral rises, and bodyweight squats. Experimental results show the excellent performance of the proposed technique.

Nonlinear System Identification: An Effective Framework Based on the Karhunen–LoÈve Transform

This paper proposes, on the basis of a rigorous mathematical formulation, a general framework that is able to define a large class of nonlinear system identifiers. This framework exploits all those relationships that intrinsically characterize a limited set of realizations, obtained by an ensemble of output signals and their parameterized inputs, by means of the separation property of the Karhunen-Loeve transform. The generality and the flexibility of the approximating mappings (ranging from traditional approximation techniques to multiresolution decompositions and neural networks) allow the design of a large number of distinct identifiers each displaying a number of properties such as linearity with respect to the parameters, noise rejection, low computational complexity of the approximation procedure. Exhaustive experimentation on specific case studies reports high identification performance for four distinct identifiers based on polynomials, splines, wavelets and radial basis functions. Several comparisons show how these identifiers almost always have higher performance than that obtained by current best practices, as well as very good accuracy, optimal noise rejection, and fast algorithmic elaboration. As an example of a real application, the identification of a voice communication channel, comprising a digital enhanced cordless telecommunications (DECT) cordless phone for wireless communications and a telephone line, is reported and discussed.

SystemC-WMS: Mixed-Signal Simulation Based on Wave Exchanges

This chapter proposes a methodology for extending SystemC to mixed-signal systems, aimed at allowing the reuse of analog models and to the simulation of heterogeneous systems. To this end, a general method for modeling analog modules using wave quantities is suggested, and a new kind of port and channel suitable to let modules communicate through waves have been defined. These entities are plugged directly on top of the standard SystemC kernel, so as to allow a seamless integration with the preexisting simulation environment, and are designed to permit total interconnection freedom to ease the development of reusable analog libraries.

A novel KLT algorithm optimized for small signal sets [speech processing applications]

The Karhunen-Loeve transform, being able to represent stochastic processes under appropriate conditions, is a powerful signal processing tool. But the high computational cost incurred in the modeling of long signals has limited its use in the recognition of speech segmented at the word level. In this paper, we present a novel algorithm that significantly reduces the computational cost when the number of signals to be treated is small in comparison to their samples.

A current mode multistable memory using asynchronous successive approximation A/D converters

In this work a multistable circuit for current memorization is presented. The technique used is based on a new type of asynchronous successive approximation A/D converter. The circuit has been implemented in a 0.8 /spl mu/m CMOS technology.

SiSMA-a tool for efficient analysis of analog CMOS integrated circuits affected by device mismatch

In this paper a simulator for the statistical analysis of analog CMOS integrated circuits affected by technological tolerance effects, including device mismatch, is presented. The tool, able to perform dc, ac, and transient analyses, is based on a rigorous formulation of circuit equations starting from the modified nodal analysis and including random current sources to take into account technological tolerances. Statistical simulation of specific circuits shows that the simulator requires a simulation time several orders of magnitude lower than that required by Monte Carlo analysis, while ensuring a good accuracy.

Speaker Identification with Short Sequences of Speech Frames

In biometric person identification systems, speaker identification plays a crucial role as the voice is the more natural signal to produce and the simplest to acquire. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to de-correlate the vocal source and the vocal tract filter make them suitable for speech recognition, they show up some drawbacks in speaker recognition. This paper presents an experimental evaluation showing that reducing the dimension of features by using the discrete Karhunen-Loeve transform (DKLT), guarantees better performance with respect to conventional MFCC features. In particular with short sequences of speech frames, that is with utterance duration of less than 1 s, the performance of truncated DKLT representation are always better than MFCC.

Representation of Nonlinear Random Transformations by Non-Gaussian Stochastic Neural Networks

The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.

System Level Modelling of RF IC in SystemC-WMS

This paper proposes a methodology for modelling and simulation of RF systems in SystemC-WMS. Analog RF modules have been described at system level only by using their specifications. A complete Bluetooth transceiver, consisting of digital and analog blocks, has been modelled and simulated using the proposed design methodology. The developed transceiver modules have been connected to the higher levels of the Bluetooth stack described in SystemC, allowing the analysis of the performance of the Bluetooth protocol at all the different layers of the protocol stack.