Relevant bibliographies by topics / Speech waveform analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Speech waveform analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Speech waveform analysis"

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Askenfelt, Anders G., and Britta Hammarberg. "Speech Waveform Perturbation Analysis." Journal of Speech, Language, and Hearing Research 29, no. 1 (March 1986): 50–64. http://dx.doi.org/10.1044/jshr.2901.50.

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The performance of seven acoustic measures of cycle-to-cycle variations (perturbations) in the speech waveform was compared. All measures were calculated automatically and applied on running speech. Three of the measures refer to the frequency of occurrence and severity of waveform perturbations in special selected parts of the speech, identified by means of the rate of change in the fundamental frequency. Three other measures refer to statistical properties of the distribution of the relative frequency differences between adjacent pitch periods. One perturbation measure refers to the percentage of consecutive pitch period differences with alternating signs. The acoustic measures were tested on tape recorded speech samples from 41 voice patients, before and after successful therapy. Scattergrams of acoustic waveform perturbation data versus an average of perceived deviant voice qualities, as rated by voice clinicians, are presented. The perturbation measures were compared with regard to the acoustic-perceptual correlation and their ability to discriminate between normal and pathological voice status. The standard deviation of the distribution of the relative frequency differences was suggested as the most useful acoustic measure of waveform perturbations for clinical applications.
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Yohanes, Banu W. "Linear Prediction and Long Term Predictor Analysis and Synthesis." Techné : Jurnal Ilmiah Elektroteknika 16, no. 01 (April 3, 2017): 49–58. http://dx.doi.org/10.31358/techne.v16i01.158.

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Spectral analysis may not provide an accurate description of speech articulation. This article presents an experimental setup of representing speech waveform directly in terms of timevarying parameters. It is related to the transfer function of the vocal tract. Linear Prediction, Long Term Predictor Analysis, and Synthesis filters are designed and implemented, as well as the theory behind introduced. The workflows of the filters are explained by detailed and codes of those filters. Original waveform files are framed with Hamming window and for each frames the filters are applied, and the reconstructed speeches are compared to original waveforms. The results come out that LP and LTP analysis can be used in DSPs due to its periodical characteristic, but some distortion might be coursed, which examined in the experiments.
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Tadic, Predrag, Zeljko Djurovic, and Branko Kovacevic. "Analysis of speech waveform quantization methods." Journal of Automatic Control 18, no. 1 (2008): 19–22. http://dx.doi.org/10.2298/jac0801019t.

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Digitalization, consisting of sampling and quantization, is the first step in any digital signal processing algorithm. In most cases, the quantization is uniform. However, having knowledge of certain stochastic attributes of the signal (namely, the probability density function, or pdf), quantization can be made more efficient, in the sense of achieving a greater signal to quantization noise ratio. This means that narrower channel bandwidths are required for transmitting a signal of the same quality. Alternatively, if signal storage is of interest, rather than transmission, considerable savings in memory space can be made. This paper presents several available methods for speech signal pdf estimation, and quantizer optimization in the sense of minimizing the quantization error power.
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Read, Charles, Eugene H. Buder, and Raymond D. Kent. "Speech Analysis Systems." Journal of Speech, Language, and Hearing Research 35, no. 2 (April 1992): 314–32. http://dx.doi.org/10.1044/jshr.3502.314.

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Performance characteristics are reviewed for seven systems marketed for acoustic speech analysis: CSpeech, CSRE, ILS-PC, Kay Elemetrics model 5500 Sona-Graph, MacSpeech Lab II, MSL, and Signalyze. The characteristics reviewed include system components, basic capabilities (signal acquisition, waveform operations, analysis, and other functions), documentation, user interface, data formats and journaling, speed and precision of spectral analysis, and speed and precision of fundamental frequency analysis. Basic capabilities are also tabulated for three recently introduced systems: the Sensimetrics SpeechStation, the Kay Elemetrics Computerized Speech Lab (CSL), and the LSI Speech Workstation. In addition to the capability and performance summaries, this article offers suggestions for continued development of speech analysis systems, particularly in data exchange, journaling, display features, spectral analysis, and fundamental frequency analysis.
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Debruyne, F., P. Delaere, J. Wouters, and P. Uwents. "Acoustic analysis of tracheo-oesophageal versus oesophageal speech." Journal of Laryngology & Otology 108, no. 4 (April 1994): 325–28. http://dx.doi.org/10.1017/s0022215100126660.

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AbstractIn order to evaluate the vocal quality of tracheo-oesophageal and oesophageal speech, several objective acoustic parameters were measured in the acoustic waveform (fundamental frequency, waveform perturbation) and in the frequency spectrum (harmonic prominence, spectral slope). Twelve patients using tracheo-oesophageal speech (with the Provox® valve) and 12 patients using oesophageal speech for at least two months, participated.The main results were that tracheo-oesophageal voices more often showed a detectable fundamental frequency, and that this fundamental frequency was fairly stable; there was also a tendency to more clearly defined harmonics in tracheo-oesophageal speech. This suggests a more regular vibratory pattern in the pharyngo-oesophageal segment, due to the more efficient respiratory drive in tracheo-oesophageal speech. So, a better quality of the voice can be expected, in addition to the longer phonation time and higher maximal intensity.
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Boggs, George J., and Michael D. Connelly. "WFORM: A graphical speech-waveform editing and analysis system." Behavior Research Methods, Instruments, & Computers 18, no. 1 (January 1986): 25–31. http://dx.doi.org/10.3758/bf03200989.

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Balakrishnan Sivakumar and Praveen Kadakola Biligirirangaiah. "Analysis of vowel addition or deletion in Continuous Speech." Global Journal of Engineering and Technology Advances 7, no. 3 (June 30, 2021): 136–43. http://dx.doi.org/10.30574/gjeta.2021.7.3.0084.

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In order to improve the recognition performance, the articulation of the transcription is very important in the process of training. For continuous speech, the essential characteristics of various speakers are pronunciation variation, over focused or inadequately highlighted words can results the waveform misalignment in the sub word unit margin. Because of the deviation in the articulation leads into misalignment when this is compared with articulation dictionary. So the deletion or insertion of the sub word is necessary. This happens because for each expression, the transcription is not precise. This paper presents the corrections in the transcription at the sub word level utilizing sound prompts that are presented in the waveform. The transcription of a word is fixed Utilizing sentence-level transcriptions with reference to the phonemes that create the word. Specifically, it clarifies that vowels are either deleted or inserted. To help the proposed contention, errors in persistent discourse are validated utilizing machine learning and signal processing tools. A programmed information driven annotator abusing the inductions drawn from the examination is utilized to address transcription errors. The outcomes show that rectified pronunciations lead to higher probability for train expressions in the TIMIT corpus.
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Zhang, Fawen, Chelsea Benson, and Steven J. Cahn. "Cortical Encoding of Timbre Changes in Cochlear Implant Users." Journal of the American Academy of Audiology 24, no. 01 (January 2013): 046–58. http://dx.doi.org/10.3766/jaaa.24.1.6.

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Background: Most cochlear implant (CI) users describe music as a noise-like and unpleasant sound. Using behavioral tests, most prior studies have shown that perception of pitch-based melody and timbre is poor in CI users. Purpose: This article will focus on cortical encoding of timbre changes in CI users, which may allow us to find solutions to further improve CI benefits. Furthermore, the value of using objective measures to reveal neural encoding of timbre changes may be reflected in this study. Research Design: A case-control study of the mismatch negativity (MMN) using electrophysiological technique was conducted. To derive MMNs, three randomly arranged oddball paradigms consisting of standard/deviant instrumental pairs: saxophone/piano, cello/trombone, and flute/French horn, respectively, were presented. Study Sample: Ten CI users and ten normal-hearing (NH) listeners participated in this study. Data Collection and Analysis: After filtering, epoching, and baseline correction, independent component analysis (ICA) was performed to remove artifacts. The averaged waveforms in response to the standard stimuli (STANDARD waveform) and the deviant stimuli (DEVIANT waveform) in each condition were separately derived. The responses from nine electrodes in the fronto-central area were averaged to form one waveform. The STANDARD waveform was subtracted from the DEVIANT waveform to derive the difference waveform, for which the MMN was judged to be present or absent. The measures used to evaluate the MMN included the MMN peak latency and amplitude as well as MMN duration. Results: The MMN, which reflects the ability to automatically detect acoustic changes, was present in all NH listeners but only approximately half of CI users. In CI users with present MMNs, the MMN peak amplitude and duration were significantly smaller and shorter compared to those in NH listeners. Conclusions: Our electrophysiological results were consistent with prior behavioral results that CI users' performance in timbre perception was significantly poorer than that in NH listeners. Our results may suggest that timbre information is poorly registered in the auditory cortex of CI users and the capability of automatic detection of timbre changes is degraded in CI users. Although there are some limitations of the MMN in CI users, along with other objective auditory evoked potential tools, the MMN may be a useful objective tool to indicate the extent of sound registration in auditory cortex in the future efforts of improving CI design and speech strategy.
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Ingram, Kelly, Ferenc Bunta, and David Ingram. "Digital Data Collection and Analysis." Language, Speech, and Hearing Services in Schools 35, no. 2 (April 2004): 112–21. http://dx.doi.org/10.1044/0161-1461(2004/013).

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Technology for digital speech recording and speech analysis is now readily available for all clinicians who use a computer. This article discusses some advantages of moving from analog to digital recordings and outlines basic recording procedures. The purpose of this article is to familiarize speech-language pathologists with computerized audio files and the benefits of working with those sound files as opposed to using analog recordings. This article addresses transcription issues and offers practical examples of various functions, such as playback, editing sound files, using waveform displays, and extracting utterances. An appendix is provided that describes step-by-step how digital recording can be done. It also provides some editing examples and a list of useful computer programs for audio editing and speech analyses. In addition, this article includes suggestions for clinical uses in both the assessment and the treatment of various speech and language disorders.
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Chi-Sang Jung, Young-Sun Joo, and Hong-Goo Kang. "Waveform Interpolation-Based Speech Analysis/Synthesis for HMM-Based TTS Systems." IEEE Signal Processing Letters 19, no. 12 (December 2012): 809–12. http://dx.doi.org/10.1109/lsp.2012.2221703.

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Dissertations / Theses on the topic "Speech waveform analysis"

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ARAUJO, ANTONIO MARCOS DE LIMA. "ANALYSIS OF WAVEFORM CODERS FOR SPEECH AND DATA SIGNALS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1986. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9246@1.

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O trabalho examina o comportamento de Codificadores de forma de onda operando a 32,56 e 64kbit/s para transmissão digital de sinais de voz e de sinais de dados PSK-8 a 4800 bit/s e QAM-16 a 9600 bit/s. A partir de uma análise detalhada dos diversos sistemas, tanto em canal ideal como um canal ruidoso, é verificada a necessidade de se fazer uma identificação do tipo de sinal. De modo a permitir sua codificação de forma mais eficiente. É, então, proposta e avaliada a utilização de uma técnica de identificação estatística de sinais de voz e dados, em codificadores de forma de onda. A incorporação desta técnica ao sistema ADPCM a 32 kbit/s recomendado pelo CCITT permite uma melhoria do desempenho para sinais de dados, sem com isso alterar sua eficiência para sinais de voz.
This thesis evaluates the performance of waveform coders at 32,56 and 64kbit/s for digital transmission of speech signal and 4800 bit/s PSK-8 and 9600 bit/s QAM-16 voiceband data signas. A detailed analysis of the systems is carried out both under ideal and noisy channel conditions. From this analysis it was found that a scheme which accurately distinguishes the two classes of signals, would allow a more efficient encoding procedure. A method of statistical identification of speech and data signals is proposed and its use in wakeform coders is, then, analysed. The incorporation of this method into the 32 kbit/s ADPCM system recommended by CCITT provides an improvement in performance for data signals, without sacrificing its efficiency for speech signal.
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Ghaidan, Khaldoon A. "A study of the application of modern techniques to speech waveform analysis." Thesis, Loughborough University, 1986. https://dspace.lboro.ac.uk/2134/28015.

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Spectrograms are perhaps the most commonly used method for studying the characteristics of speech waveforms. Producing a spectrogram can conveniently be divided into two parts, the analysis and the display, and this thesis describes a study of both these aspects.
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Carandang, Alfonso B., and n/a. "Recognition of phonemes using shapes of speech waveforms in WAL." University of Canberra. Information Sciences & Engineering, 1994. http://erl.canberra.edu.au./public/adt-AUC20060626.144432.

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Generating a phonetic transcription of the speech waveform is one method which can be applied to continuous speech recognition. Current methods of labelling a speech wave involve the use of techniques based on spectrographic analysis. This paper presents a computationally simple method by which some phonemes can be identified primarily by their shapes. Three shapes which are regularly manifested by three phonemes were examined in utterances made by a number of speakers. Features were then devised to recognise their patterns using finite state automata combined with a checking mechanism. These were implemented in the Wave Analysis Language (WAL) system developed at the University of Canberra and the results showed that the phonemes can be recognised with high accuracy. The resulting shape features have also demonstrated a degree of speaker independence and context dependency.
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Liu, Lup Shun Nelson Carleton University Dissertation Engineering Electronics. "Sensitivity analysis and optimization of high-speed VLSI interconnects using asymptotic waveform evaluation." Ottawa, 1993.

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Schulz, Yvonne Katrin [Verfasser], Stefan [Akademischer Betreuer] Kniesburges, and Stefan [Gutachter] Kniesburges. "Parameter analysis of the Glottal Area Waveform based on high-speed recordings within a synthetic larynx model / Yvonne Katrin Schulz ; Gutachter: Stefan Kniesburges ; Betreuer: Stefan Kniesburges." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2020. http://d-nb.info/120337769X/34.

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Narayanan, G. "Synchronised Pulsewidth Modulation Strategies Based On Space Vector Approach For Induction Motor Drives." Thesis, Indian Institute of Science, 1999. http://hdl.handle.net/2005/139.

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In high power induction motor drives, the switching frequency of the inverter is quite low due to the high losses in the power devices. Real-time PWM strategies, which result in reduced harmonic distortion under low switching frequencies and have maximum possible DC bus utilisation, are developed for such drives in the present work. The space vector approach is taken up for the generation of synchronised PWM waveforms with 3-Phase Symmetry, Half Wave Symmetry and Quarter Wave Symmetry, required for high-power drives. Rules for synchronisation and the waveform symmetries are brought out. These rules are applied to the conventional and modified forms of space vector modulation, leading to the synchronised conventional space vector strategy and the Basic Bus Clamping Strategy-I, respectively. Further, four new synchronised, bus-clamping PWM strategies, namely Asymmetric Zero-Changing Strategy, Boundary Sampling Strategy-I, Basic Bus Clamping Strategy-II and Boundary Sampling Strategy-II, are proposed. These strategies exploit the flexibilities offered by the space vector approach like double-switching of a phase within a subcycle, clamping of two phases within a subcycle etc. It is shown that the PWM waveforms generated by these strategies cannot be generated by comparing suitable 3-phase modulating waves with a triangular carrier wave. A modified two-zone approach to overmodulation is proposed. This is applied to the six synchronised PWM strategies, dealt with in the present work, to extend the operation of these strategies upto the six-step mode. Linearity is ensured between the magnitude of the reference and the fundamental voltage generated in the whole range of modulation upto the six-step mode. This is verified experimentally. A suitable combination of these strategies leads to a significant reduction in the harmonic distortion of the drive at medium and high speed ranges over the conventional space vector strategy. This reduction in harmonic distortion is demonstrated, theoretically as well as experimentally, on a constant V/F drive of base frequency 50Hz for three values of maximum switching frequency of the inverter, namely 450Hz, 350Hz and 250Hz. Based on the notion of stator flux ripple, analytical closed-form expressions are derived for the harmonic distortion due to the different PWM strategies. The values of harmonic distortion, computed based on these analytical expressions, compare well with those calculated based on Fourier analysis and those measured experimentally.
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Zheng, Yu-Xiang, and 鄭宇翔. "Analysis of Time-domain Waveform for Stub Effect in High-Speed Digital Circuits." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/p7u4d6.

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碩士
中原大學
電子工程研究所
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This paper investigates how via stubs effect the time-domain transmission (TDT) waveform, Time-Domain Reflectometry (TDR) waveform, S21, S11, and eye diagram in high speed circuit. Due to the limitations of the laboratory process, the via stub was changed to open stub. In order to understand the effect on the waveform, various parameters of the open stub are analysed. The manner in which the time-domain reflection noise affects the TDR waveform and the step voltage on the TDT waveform is investigated by lattice diagram. And in special cases, waveform overlays are used to explain the behavior of time-domain transmission waveforms. The effects of mismatch stub length due to layer change on the TDT waveform are investigated. Observe the effect on the rise time by changing the Number of open stubs. Propose the eye mask in order to create the design chart. Formulas for TDT waveform are derived by lattice diagram and the waveform overlays.
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Books on the topic "Speech waveform analysis"

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Ghaidan, K. A. A study of the application of modern techniques to speech waveform analysis. 1986.

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Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, and acoustic and language models, which are estimated in a training phase, and the decoder. The focus of this article is on methods used in state-of-the-art speaker-independent, large-vocabulary continuous speech recognition (LVCSR). Primary application areas for such technology are dictation, spoken language dialogue, and transcription for information archival and retrieval systems. Finally, this article discusses issues and directions of future research.
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Book chapters on the topic "Speech waveform analysis"

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Xiong, Yan, Fang Xu, Qiang Chen, and Jun Zhang. "Speech Enhancement Using Heterogeneous Information." In Cognitive Analytics, 1060–74. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch054.

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This article describes how to use heterogeneous information in speech enhancement. In most of the current speech enhancement systems, clean speeches are recovered only from the signals collected by acoustic microphones, which will be greatly affected by the acoustic noises. However, heterogeneous information from different kinds of sensors, which is usually called the “multi-stream,” are seldom used in speech enhancement because the speech waveforms cannot be recovered from the signals provided by many kinds of sensors. In this article, the authors propose a new model-based multi-stream speech enhancement framework that can make use of the heterogeneous information provided by the signals from different kinds of sensors even when some of them are not directly related to the speech waveform. Then a new speech enhancement scheme using the acoustic and throat microphone recordings is also proposed based on the new speech enhancement framework. Experimental results show that the proposed scheme outperforms several single-stream speech enhancement methods in different noisy environments.
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Saloni, Saloni, Rajender K. Sharma, and Anil K. Gupta. "Human Voice Waveform Analysis for Categorization of Healthy and Parkinson Subjects." In Biomedical Engineering, 397–411. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3158-6.ch017.

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Parkinson disease is a neurological disorder. In this disease control over body muscles get disturbed. In almost 90% of the cases, people suffering from Parkinson disease (PD) have speech disorders. The goal of the paper is to differentiate healthy and PD affected persons using voice analysis. There are no well-developed lab techniques available for Parkinson detection. Parkinson detection using voice analysis is a noninvasive, reliable and economic method. Using this technique patient need not to visit the clinic. In this paper the authors have recorded 155 phonations from 25 healthy and 22 PD affected persons. Classification is done using two proposed parameters: Local angular frequency and instantaneous deviation in the waveform. Support vector machine is used as a classifier. Maximum 86.8% classification accuracy is achieved using linear kernel function.
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Conference papers on the topic "Speech waveform analysis"

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Balaji, V., and G. Sadashivappa. "Waveform Analysis and Feature Extraction from Speech Data of Dysarthric Persons." In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2019. http://dx.doi.org/10.1109/spin.2019.8711768.

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Takizawa, Y., and A. Fukasawa. "Parametric analysis methods of time-variant waveform and its pattern." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226636.

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Wang, Chao, Jian Wang, and Xudong Zhang. "Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952594.

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Karami, M. Amin, Ehsan T. Esfahani, Mohsen Daghooghi, and Iman Borazjani. "Self-Assembling Swimming Smart Boxes." In ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smasis2014-7533.

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This paper presents vibration analysis and structural optimization of a self-assembled structure for swimming. The mode shapes of the structure resemble the body waveform of a swimming Mackerel fish. The lateral deformation waveform of the body of Mackerel is extracted from literature. At higher swimming speeds fish generate the waveform at a higher frequency. Their body waveform stays the same at almost all normal swimming speeds. At the final destination, the box self-assembles using shape memory alloys. The shape memory alloys used for configuration change of the box robot cannot be used for swimming since they fail to operate at high frequencies. MFCs are actuated at the fundamental natural frequency of the structure. This excites the primary mode of resonance. The primary mode of resonance involves rotations of the joints of the robot in the desired fashion. The MFCs are therefore used to indirectly generate the body waveform. We optimize the thickness of the panels and the stiffness of the joints to most efficiently generate the swimming waveforms. Unlike eel we change the speed of the robot by changing the amplitude of the body motions. This is because the frequency of the motion is fixed to the first natural frequency of the robot. The swimming box can swim over the surface and can also swim underwater. With slight modification the boxes can crawl or slither over the land.
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Venkat Krishnan, Ravikumar, Seah Yi Xuan, Lim Gabriel, Tan Abel, Lua Winson, Gopinath Ranganathan, Phoa Angeline, and Chua Choon Meng. "Pattern Search Automation for Combinational Logic Analysis." In ISTFA 2018. ASM International, 2018. http://dx.doi.org/10.31399/asm.cp.istfa2018p0086.

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Abstract Combinational logic analysis (CLA) using laser voltage probing allows studying standard cells such as NOR or NAND gates as a whole, instead of individual transistors. The process involves building a reference library of laser probing (LP) waveforms and comparing them to signals from the real device. While CLA has greatly increased the success rate and turn-around time for LP, there are difficulties in signal interpretation. This is partly due to the lack of precise understanding of the laser interaction area and probe placement and partly due to difficulties identifying the correct logic states in the waveform. In this work, we have significantly improved the CLA process by first predicting the shape of the waveform based on laser interaction with the target circuitry and second, implementing an automated pattern search algorithm to further increase the speed and reliability of CLA using LP.
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Cummings, K. E., and M. A. Clements. "Improvements to and applications of analysis of stressed speech using glottal waveforms." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226129.

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Milne and Pace. "Wigner distribution detection and analysis of FMCW and P-4 polyphase LPI waveforms." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004781.

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Olivadese, Salvatore Bernardo, and Stefano Grivet-Talocia. "Transient analysis of high-speed channels via Newton-GMRES Waveform Relaxation." In 2012 IEEE 21st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS). IEEE, 2012. http://dx.doi.org/10.1109/epeps.2012.6457886.

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Dhindsa, Harjot, Arvind Sridhar, Ram Achar, Michel Nakhla, and Douglas Paul. "Transient Analysis of Power Grid Networks via Waveform Relaxation Techniques." In 2009 International Microwave Workshop Series on Signal Integrity and High-Speed Interconnects (IMWS). IEEE, 2009. http://dx.doi.org/10.1109/imws.2009.4814916.

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Ponti, Fabrizio, and Luca Solieri. "Analysis of the Interactions Between Indicated and Reciprocating Torques for the Development of a Torsional Behavior Model of the Powertrain." In ASME 2007 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/icef2007-1809.

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Torque-based engine control systems usually employ a produced torque estimation feedback in order to verify that the strategy target torque has been met. Torque estimation can be performed using static maps describing the engine behaviour or using models describing the existing relationships between signals measured on the engine and the indicated torque produced. Signals containing information on the combustion development, suitable for this purpose, are, among other, the ion-current signal, the vibration signals obtained from accelerometers mounted on the engine block, or the instantaneous engine speed fluctuations. This paper presents the development and the identification process of an engine-driveline torsional behavior model that enables indicated torque estimation from instantaneous engine speed measurement. Particular attention has been devoted to the interactions between indicated and reciprocating torques, and their effects over instantaneous engine speed fluctuations. Indicated and reciprocating torques produce, in fact, opposite excitations on the driveline that show opposite effects on the engine speed waveform: for low engine speed usually indicated torque prevails, while the opposite applies for higher engine speed. In order to correctly estimate indicated torque from engine speed measurement it is therefore necessary to correctly evaluate the reciprocating torque contribution. Reciprocating torque is usually described using a waveform as a function of crank angle, while its amplitude depends on the value of the reciprocating masses. As mentioned before, knowledge of the reciprocating masses is fundamental in order to obtain correct estimation of the indicated torque. The identification process that has been setup for the engine-driveline torsional model enables to evaluate the relationship between torques applied to the engine and the corresponding engine speed waveform even without knowing the value of the reciprocating masses. In addition, once this model has been setup, it is possible to estimate with high precision the value of the reciprocating masses. Particular attention has been devoted also to the feasibility of the application of the identified model on-board for torque estimation; for this reason the model has been developed in a very simple form. The approach proved to be effective both on gasoline and diesel engine, both for engine mounted on a test cell and on-board, with different engine configurations. Examples of application are given for some of the configurations investigated.
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Reports on the topic "Speech waveform analysis"

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Drive modelling and performance estimation of IPM motor using SVPWM and Six-step Control Strategy. SAE International, April 2021. http://dx.doi.org/10.4271/2021-01-0775.

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This paper presents a comprehensive evaluation of the performance of an interior permanent magnet (IPM) traction motor drive, and analyses the impact of different modulation techniques. The most widely used modulation methods in traction motor drives are Space vector modulation (SVPWM), over-modulation, and six-step modulation have been implemented. A two-dimensional electromagnetic finite element model of the motor is co-simulated with a dynamic model of a field-oriented control (FOC) circuit. For accurate tuning of the current controllers, extended complex vector synchronous frame current regulators are employed. The DC-link voltage utilization, harmonics in the output waveforms, torque ripple, iron losses, and AC copper losses are calculated and compared with sinusoidal excitation. Overall, it is concluded that the selection of modulation technique is related to the operating condition and motor speed, and a smooth transition between different modulation techniques is essential to achieve a better performance.
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