Nowadays vehicle design paradigm influences not only the effectiveness of the different means of transport, but also the environment and economy in a critical way. The assessment of the consequences that design choices have on society at large are necessary to understand the limits of the methods and techniques currently employed. One of the mechanisms set in motion is the planned obsolescence of products and services. This has affected vehicle design paradigm in such a way that the variety in the market has shadowed the primary function of vehicle systems: the transport of persons and goods. Amongst the consequences of the expansion of such market is the exponential rise on combustion emissions to the atmosphere, which has become a great hindrance for humans health and survival of ecosystems. The development of evaluation tools for such consequences and their piloting mechanisms is needed so as to implement an ECO2  (Ecological and Economical) vehicle design paradigm. Moreover, the multifunctional design paradigm that drives aeronautical and vehicle engineering is an ever-growing demand of smart materials and structures, able to fulfil multiple requirements in an effective way. The understanding of certain phenomena intrinsic to the introduction of novel materials has found certain limits due to the complexity of the models needed. This work presents as a first step an assessment of the causes and consequences of the vehicle exponential market growth based on the analysis of the planned obsolescence within. Furthermore, a method for the acoustic response analysis of multilayered structures including anisotropic poroelastic materials is introduced. The methodology consists in a plane wave approach as a base for introducing the complex mechanic and acoustic equations governing anisotropic homogeneous media, e.g. open-celled foams, into an alternative mathematical tool manipulating physical wave amplitudes propagation within the studied media. In addition, this method is coupled to a power partitioning and energetic assessment tool so as to understand the phenomena present in complex multilayered designs. / <p>QC 20150323</p>

<p>Intake and Exhaust system noise makes a huge contribution to the interior and exterior noise of automobiles. There are a number of linear acoustic tools developed by institutions and industries to predict the acoustic properties of intake and exhaust systems. The present project discusses and validates, through measurements, the proper modelling of these systems using BOOST-SID and discusses the ideas to properly convert a geometrical model of an exhaust muffler to an acoustic model. The various elements and their properties are also discussed.</p><p>When it comes to Acoustic properties there are several parameters that describe the performance of a muffler, the Transmission Loss (TL) can be useful to check the validity of a mathematical model but when we want to predict the actual acoustic behavior of a component after it is installed in a system and subjected to operating conditions then we have to determine other properties like Attenuation, Insertion loss etc,.</p><p>Zero flow and Mean flow (M=0.12) measurements of these properties were carried out for mufflers ranging from simple expansion chambers to complex geometry using two approaches 1) Two Load technique 2) Two Source location technique. For both these cases, the measured transmission losses were compared to those obtained from BOOST-SID models.</p><p>The measured acoustic properties compared well with the simulated model for almost all the cases.</p>

Darbas „Lietuvių šnekos atpažinimo akustinis modeliavimas“ yra skirtas lietuvių šnekos atpažinimo akustiniam modeliavimui. Darbe buvo tirtas žodžiais, skiemenimis, kontekstiniais skiemenimis, fonemomis ir kontekstinėmis fonemomis grįstas šnekos atpažinimas. Tyrimai atlikti izoliuotiems žodžiams ir ištisinei šnekai. Iki šiol lietuvių šnekos atpažinime populiariausi kalbos vienetai buvo fonema ir kontekstinė fonema, o kitų kalbos vienetų analizė nebuvo atliekama. Šiame darbe siekiama palyginti lingvistinio tipo kalbos vienetų gebėjimą modeliuoti šneką ir parodyti, kad kalbos vienetų analizė siūlo alternatyvius fonemai ir kontekstinei fonemai kalbos vienetus. Darbe pasiūlyta metodika mišriam skiemenų ir fonemų akustiniam modeliavimui, naujas kalbos vienetas – pseudo-skiemuo; technologijos atskirų kalbos vienetų akustiniam modeliavimui (schemos, įrankiai, rekomendacijos). Eksperimentiniams tyrimams atlikti paruoštas izoliuotų žodžių garsynas ir sukurtos dvi ištisinės šnekos garsyno LRN versijos. Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, nustatyta, kad modelių mokymo aibės dydis, akustinių modelių mokymo aibės turinys daro įtaką šnekos atpažinimo tikslumui. Pateikiamos rekomendacijos akustiniam modeliavimui žodžių pagrindu. Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, skiemenims ir fonemoms, gauti rezultatai 98 ±1,8 % tikslumu siejami su skiemens tipo kalbos vienetais. Dėl skiemenų akustinio modeliavimo... [toliau žr. visą tekstą] / This paper is devoted to an acoustic modelling of Lithuanian speech recognition. Word-, syllable-, contextual syllable-, phoneme- and contextual phoneme-based speech recognition was investigated. Investigations were performed for isolated words and continuous speech. The most popular sub-word units in Lithuanian speech recognition are phonemes and contextual phonemes, and research on other sub-word units is omitted. This paper aims to compare capacity of linguistic sub-word units to model speech and to demonstrate that investigation of sub-word units suggest using alternative sub-word units to phoneme and contextual phoneme. The dissertation proposes a new methodology for acoustic modelling of syllables and phonemes, new sub-word unit – pseudo-syllable; technologies for acoustic modelling of separate sub-word units, including developed schemes, tools and recommendations. Speech corpus of isolated words was prepared and two versions of corpus of continuous speech LRN were developed for experimental research. Investigation of recognition of isolated words and construction of acoustic models for words showed that a size of training set of acoustic models, a content of training set in regard to number of speakers have an influence on speech recognition accuracy. The recommendations for word-based acoustic modelling are given. Investigation of recognition of isolated words and construction of acoustic models for words, syllables and phonemes showed that the best recognition... [to full text]

This paper is devoted to an acoustic modelling of Lithuanian speech recognition. Word-, syllable-, contextual syllable-, phoneme- and contextual phoneme-based speech recognition was investigated. Investigations were performed for isolated words and continuous speech. The most popular sub-word units in Lithuanian speech recognition are phonemes and contextual phonemes, and research on other sub-word units is omitted. This paper aims to compare capacity of linguistic sub-word units to model speech and to demonstrate that investigation of sub-word units suggest using alternative sub-word units to phoneme and contextual phoneme. The dissertation proposes a new methodology for acoustic modelling of syllables and phonemes, new sub-word unit – pseudo-syllable; technologies for acoustic modelling of separate sub-word units, including developed schemes, tools and recommendations. Speech corpus of isolated words was prepared and two versions of corpus of continuous speech LRN were developed for experimental research. Investigation of recognition of isolated words and construction of acoustic models for words showed that a size of training set of acoustic models, a content of training set in regard to number of speakers have an influence on speech recognition accuracy. The recommendations for word-based acoustic modelling are given. Investigation of recognition of isolated words and construction of acoustic models for words, syllables and phonemes showed that the best recognition... [to full text] / Darbas „Lietuvių šnekos atpažinimo akustinis modeliavimas“ yra skirtas lietuvių šnekos atpažinimo akustiniam modeliavimui. Darbe buvo tirtas žodžiais, skiemenimis, kontekstiniais skiemenimis, fonemomis ir kontekstinėmis fonemomis grįstas šnekos atpažinimas. Tyrimai atlikti izoliuotiems žodžiams ir ištisinei šnekai. Iki šiol lietuvių šnekos atpažinime populiariausi kalbos vienetai buvo fonema ir kontekstinė fonema, o kitų kalbos vienetų analizė nebuvo atliekama. Šiame darbe siekiama palyginti lingvistinio tipo kalbos vienetų gebėjimą modeliuoti šneką ir parodyti, kad kalbos vienetų analizė siūlo alternatyvius fonemai ir kontekstinei fonemai kalbos vienetus. Darbe pasiūlyta metodika mišriam skiemenų ir fonemų akustiniam modeliavimui, naujas kalbos vienetas – pseudo-skiemuo; technologijos atskirų kalbos vienetų akustiniam modeliavimui (schemos, įrankiai, rekomendacijos). Eksperimentiniams tyrimams atlikti paruoštas izoliuotų žodžių garsynas ir sukurtos dvi ištisinės šnekos garsyno LRN versijos. Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, nustatyta, kad modelių mokymo aibės dydis, akustinių modelių mokymo aibės turinys daro įtaką šnekos atpažinimo tikslumui. Pateikiamos rekomendacijos akustiniam modeliavimui žodžių pagrindu. Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, skiemenims ir fonemoms, gauti rezultatai 98 ±1,8 % tikslumu siejami su skiemens tipo kalbos vienetais. Dėl skiemenų akustinio modeliavimo... [toliau žr. visą tekstą]

V dnešnej dobe systémy rozpoznávania reči s veľkým slovníkom dosahujú pomerne vysoké presnosti. Za ich výsledkami však často stoja desiatky ba až stovky hodín manuálne oanotovaných trénovacích dát. Takéto dáta sú často bežne nedostupné alebo pre požadovaný jazyk vôbec neexistujú. Možným riešením je použitie bežne dostupných no menej kvalitných audiovizuálnych dát. Táto práca sa zaoberá technikou zpracovania práve takýchto dát a ich použitím pre trénovanie akustických modelov. Ďalej táto práca pojednáva o možnom využití týchto dát pre kontinuálne vylepšovanie modelov, kedže tieto dáta sú prakticky nevyčerpateľné. Pre tieto účely bol v rámci práce navrhnutý nový prístup pre výber dát.

Intake and Exhaust system noise makes a huge contribution to the interior and exterior noise of automobiles. There are a number of linear acoustic tools developed by institutions and industries to predict the acoustic properties of intake and exhaust systems. The present project discusses and validates, through measurements, the proper modelling of these systems using BOOST-SID and discusses the ideas to properly convert a geometrical model of an exhaust muffler to an acoustic model. The various elements and their properties are also discussed. When it comes to Acoustic properties there are several parameters that describe the performance of a muffler, the Transmission Loss (TL) can be useful to check the validity of a mathematical model but when we want to predict the actual acoustic behavior of a component after it is installed in a system and subjected to operating conditions then we have to determine other properties like Attenuation, Insertion loss etc,. Zero flow and Mean flow (M=0.12) measurements of these properties were carried out for mufflers ranging from simple expansion chambers to complex geometry using two approaches 1) Two Load technique 2) Two Source location technique. For both these cases, the measured transmission losses were compared to those obtained from BOOST-SID models. The measured acoustic properties compared well with the simulated model for almost all the cases.

Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. Of particular interest are probabilistic descriptions, which enable us to represent uncertainty and random variation inherent to the world around us. This thesis presents and expands upon some tools for creating probabilistic models of sequences, with an eye towards applications involving speech and language. Modelling speech and language is not only of use for creating listening, reading, talking, and writing machines---for instance allowing human-friendly interfaces to future computational intelligences and smart devices of today---but probabilistic models may also ultimately tell us something about ourselves and the world we occupy. The central theme of the thesis is the creation of new or improved models more appropriate for our intended applications, by weakening limiting and questionable assumptions made by standard modelling techniques. One contribution of this thesis examines causal-state splitting reconstruction (CSSR), an algorithm for learning discrete-valued sequence models whose states are minimal sufficient statistics for prediction. Unlike many traditional techniques, CSSR does not require the number of process states to be specified a priori, but builds a pattern vocabulary from data alone, making it applicable for language acquisition and the identification of stochastic grammars. A paper in the thesis shows that CSSR handles noise and errors expected in natural data poorly, but that the learner can be extended in a simple manner to yield more robust and stable results also in the presence of corruptions. Even when the complexities of language are put aside, challenges remain. The seemingly simple task of accurately describing human speech signals, so that natural synthetic speech can be generated, has proved difficult, as humans are highly attuned to what speech should sound like. Two papers in the thesis therefore study nonparametric techniques suitable for improved acoustic modelling of speech for synthesis applications. Each of the two papers targets a known-incorrect assumption of established methods, based on the hypothesis that nonparametric techniques can better represent and recreate essential characteristics of natural speech. In the first paper of the pair, Gaussian process dynamical models (GPDMs), nonlinear, continuous state-space dynamical models based on Gaussian processes, are shown to better replicate voiced speech, without traditional dynamical features or assumptions that cepstral parameters follow linear autoregressive processes. Additional dimensions of the state-space are able to represent other salient signal aspects such as prosodic variation. The second paper, meanwhile, introduces KDE-HMMs, asymptotically-consistent Markov models for continuous-valued data based on kernel density estimation, that additionally have been extended with a fixed-cardinality discrete hidden state. This construction is shown to provide improved probabilistic descriptions of nonlinear time series, compared to reference models from different paradigms. The hidden state can be used to control process output, making KDE-HMMs compelling as a probabilistic alternative to hybrid speech-synthesis approaches. A final paper of the thesis discusses how models can be improved even when one is restricted to a fundamentally imperfect model class. Minimum entropy rate simplification (MERS), an information-theoretic scheme for postprocessing models for generative applications involving both speech and text, is introduced. MERS reduces the entropy rate of a model while remaining as close as possible to the starting model. This is shown to produce simplified models that concentrate on the most common and characteristic behaviours, and provides a continuum of simplifications between the original model and zero-entropy, completely predictable output. As the tails of fitted distributions may be inflated by noise or empirical variability that a model has failed to capture, MERS's ability to concentrate on high-probability output is also demonstrated to be useful for denoising models trained on disturbed data. / <p>QC 20131128</p> / ACORNS: Acquisition of Communication and Recognition Skills / LISTA – The Listening Talker