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Innovative Digital Signal Processing Methodologies for Identification and Analysis of Real Audio Systems
Get PDFEsistono molti sistemi audio reali e ciascuno ha le proprie caratteristiche ma tutti sono accomunati dal fatto che sono sistemi in grado di generare o modificare un suono. Se un sistema naturale o artificiale può essere definito come sistema sonoro, allora è possibile applicare le tecniche del digital signal processing per studiare il sistema ed emularne il comportamento. In questo lavoro di tesi si propone di introdurre delle metodologie innovative di processamento del segnale applicate ad alcuni sistemi sonori reali.
In particolare, vengono studiati e discussi tre diversi sistemi: il mondo dei dispositivi non lineari basati su valvole, con particolare attenzione agli amplificatori per chitarra e hi-fi, l'ambiente acustico di una stanza ed il suo effetto sulla propagazione del suono ed infine il suono emesso dalle api in un alveare.
Per quanto riguarda il primo sistema, vengono proposti dei contributi innovativi per l'identificazione di modelli come la serie di Volterra ed il modello di Hammerstein; in particolare viene discusso un approccio per superare alcune limitazioni dell'identificazione tramite serie di Volterra e l'applicazione di una struttura in sottobande per ridurre il costo computazionale e incrementare la velocità di convergenza di un algoritmo adattativo per l'identificazione del modello di Hammerstein. In ultima analisi, viene proposto un approccio innovativo in grado di stimare con una singola misura vari parametri di distorsione sfruttando un modello di Hammerstein generallizato.
Per quanto riguarda il secondo ambito, vengono proposti i risultati relativi a due applicazioni di equalizzazione multipunto: nel primo caso si mostrerà come l'equalizzazione può essere usata non solo per compensare le anomalie sonore generate all'interno di una stanza, ma anche per migliorare la risposta in frequenza di particolari trasduttori a vibrazione ancorati ad un pannello rigido; nel secondo caso si illustra come un approccio in sottobande può migliorare l'efficienza computazionale e la velocità di un algoritmo di equalizzazione adattativo multipunto e multicanale.
Infine, viene presentato un sistema sonoro naturale, ovvero quello generato da un alveare. In questo caso si illustrerà un sistema di acquisizione innovativo sviluppato per monitorare gli alveari con particolare attenzione al suono; succesivamente si mostrano gli approcci messi a punto per analizzare il suono registrato in due condizioni reali ed infine verranno si illustrano i risultati ottenuti grazie allo studio del suono usando algoritmi di classificazione.
Inoltre, nella parte finale dell'elaborato sono presenti dei contributi secondari ma che hanno comunque come focus principale il signal processing applicato ad ambienti acustici reali, in particolare si discute di un'implementazione di un algoritmo di cancellazione attiva del rumore e di due algoritmi per effetti digitali in cui il primo migliora le performance sonore di altoparlanti compatti, ed il secondo genera un effetto stereofonico per chitarra elettrica.Many real word audio systems exist, each has its own characteristics but almost all of them can be identified from the fact that they are able to generate or modify a sound.
If a natural or artificial system can be defined as a sound system, then it is possible to apply the techniques of digital signal processing for the studying and the emulation of the system. In this thesis, innovative methodologies for digital signal processing applied to real audio systems will be discussed.
In particular, three different audio systems will be considered: the world of vacuum-based non linear audio devices with particular attention to guitar and hi-fi amplifiers; the room acoustic environment and its effect on the sound propagation; and finally the sound emitted by honey bees in a beehive.
Regarding the first system, innovative approaches for the identification of the Volterra series and Hammerstein models will be proposed, in particular an approach to overcome some limitation of Volterra series identification. The application of a sub-band structure to reduce the computational cost and increase the convergence speed of an adaptive Hammerstein model identification will be proposed as well. Finally, an innovative approach for the measurement of several distortion parameters using a single measure, exploiting a generalized Hammerstein model, will be presented.
For the second system, the results of the application of a multi-point equalizer to two different situations will be exposed. In particular, in the first case, it will be shown how a multi-point equalization can be used not only to compensate the acoustical anomalies of a room, but also to improve the frequency response of vibrating transducers mounted on a rigid surface. The second contribution will show how a sub-band approach can be used to improve the computational cost and the speed of an adaptive algorithm for a multi-point and multi channel equalizer.
At the end, the focus will be on a natural sound system, i.e., a honey bees colony. In this case, an innovative acquisition system for honey bees sound monitoring will be presented. Then, the approaches developed for sound analysis will be exposed and applied to the recorded sounds in two different situations. Finally, the obtained results, achieved with the application of classification algorithms, will be exposed.
In the final part of the work some minor contributions still related to signal processing applied to real sound systems are presented. In particular, an implementation of an active noise control system is discussed, and two algorithms for digital effects where the former improves the sound performances of compact loudspeakers and the latter generates a stereophonic effect for electric guitars are exposed
On the importance of the sound emitted by honey bee hives
Get PDFRecent years have seen a worsening in the decline of honey bees (Apis mellifera L.) colonies. This phenomenon has sparked a great amount of attention regarding the need for intense bee hive monitoring, in order to identify possible causes, and design corresponding countermeasures. Honey bees have a key role in pollination services of both cultivated and spontaneous flora, and the increase in bee mortality could lead to an ecological and economical damage. Despite many smart monitoring systems for honey bees and bee hives, relying on different sensors and measured quantities, have been proposed over the years, the most promising ones are based on sound analysis. Sounds are used by the bees to communicate within the hive, and their analysis can reveal useful information to understand the colony health status and to detect sudden variations, just by using a simple microphone and an acquisition system. The work here presented aims to provide a review of the most interesting approaches proposed over the years for honey bees sound analysis and the type of knowledge about bees that can be extracted from sounds
Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters
Get PDFThe paper introduces a novel family of deterministic periodic signals, the orthogonal periodic sequences (OPSs), that allow the perfect identification on a finite period of any functional link polynomials (FLiP) filter with the cross-correlation method. The class of FLiP filters is very broad and includes many popular nonlinear filters, as the well-known Volterra and the Wiener nonlinear filters. The novel sequences share many properties of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of FLiP filters with the cross-correlation method. But, while PPSs exist only for orthogonal FLiP filters, the OPSs allow also the identification of non-orthogonal FLiP filters, as the Volterra filters. In OPSs, the modeled system input can be any persistently exciting sequence and can also be a quantized sequence. Moreover, OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller that PPSs. The provided experimental results, involving the identification of real devices and of a benchmark model, highlight the potentialities of the proposed OPSs in modeling unknown nonlinear systems
Experiences in numerical simulation of wax deposition in oil and multiphase pipelines: Theory versus reality
No full textFeatures Extraction Applied to the Analysis of the Sounds Emitted by Honey Bees in a Beehive
No full textNonlinear System Identification Using Wiener Basis Functions and Multiple-Variance Perfect Sequences
Get PDFThe paper addresses nonlinear identification using the Wiener series. Differently from the traditional approach, the truncated Wiener series is expressed as a linear combination of basis functions, which are orthogonal for white Gaussian inputs. The coefficients of the basis functions are efficiently estimated with the cross-correlation method, computing the cross-correlation between the basis functions and the system output. Perfect periodic sequences (PPSs), which are periodic sequences guaranteeing the perfect orthogonality of the basis functions over a period, are also developed. The PPSs allow to avoid the estimation problems experienced with the cross-correlation method using stochastic inputs. The Wiener series formulation in terms of basis functions allows also to develop a novel, more efficient, multiple-variance identification method. Multiple-variance methods exploit input signals with multiple variances for estimating the Volterra kernels. They overcome the problem of locality of the solution, i.e., the fact that the identified model well approximates the nonlinear system only for input signal variances close to that used for the identification. Optimal values of the multiple variances are also studied in the paper. Experimental results, involving the identifications of real devices, show that the proposed approach can accurately model the identified system on a wide range of input variances
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