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Relevant bibliographies by topics / Speech waveforms

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Academic literature on the topic 'Speech waveforms'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Speech waveforms"

1

Milenkovic, Paul. "Least Mean Square Measures of Voice Perturbation." Journal of Speech, Language, and Hearing Research 30, no. 4 (December 1987): 529–38. http://dx.doi.org/10.1044/jshr.3004.529.

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A signal processing technique is described for measuring the jitter, shimmer, and signal-to-noise ratio of sustained vowels. The measures are derived from the least mean square fit of a waveform model to the digitized speech waveform. The speech waveform is digitized at an 8.3 kHz sampling rate, and an interpolation technique is used to improve the temporal resolution of the model fit. The ability of these procedures to measure low levels of perturbation is evaluated both on synthetic speech waveforms and on the speech recorded from subjects with normal voice characteristics.

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Coorman, Geert. "Speech synthesis using concatenation of speech waveforms." Journal of the Acoustical Society of America 124, no. 6 (2008): 3371. http://dx.doi.org/10.1121/1.3047443.

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Ferit Gigi, Ercan. "Speech Synthesis Using Concatenation Of Speech Waveforms." Journal of the Acoustical Society of America 129, no. 1 (2011): 545. http://dx.doi.org/10.1121/1.3554813.

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Coorman, Geert. "Speech synthesis using concatenation of speech waveforms." Journal of the Acoustical Society of America 116, no. 3 (2004): 1331. http://dx.doi.org/10.1121/1.1809938.

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Xiong, Yan, Fang Xu, Qiang Chen, and Jun Zhang. "Speech Enhancement Using Heterogeneous Information." International Journal of Grid and High Performance Computing 10, no. 3 (July 2018): 46–59. http://dx.doi.org/10.4018/ijghpc.2018070104.

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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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Kleijn, W. B. "Encoding speech using prototype waveforms." IEEE Transactions on Speech and Audio Processing 1, no. 4 (1993): 386–99. http://dx.doi.org/10.1109/89.242484.

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Maddieson, Ian. "Commentary on ‘Reading waveforms’." Journal of the International Phonetic Association 21, no. 2 (December 1991): 89–91. http://dx.doi.org/10.1017/s0025100300004436.

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The previous issue of the Journal contained a discussion by Peter Ladefoged about interpreting the information in a speech waveform (JIPA, 21, 32–34), noting that examination of waveforms displays is becoming commonplace with the easy availability of personal computer tools for digitizing and editing. As Ladefoged noted:“Several aspects of sounds are clearly distinguishable from the waveforms of a phrase. Stop closures are very evident, as are differences between voiced sounds which have repetitive waveforms and voiceless sounds which do not. Differences in amplitude can be used to distinguish high frequency, high intensity sibilants from lower intensity non-sibilant fricatives; and nasals and laterals usually have smaller amplitudes than the louder adjacent vowels. An expanded view of the waveform allows us to see intervals between peaks in the damped wave of a voiced sound, and thus to calculate the frequency of the first formant. Nasals can often be distinguished from vowels in these expanded waveforms, not only by their smaller amplitudes but also by the less clear formant structure.”

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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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Arda, Betul, Daniel Rudoy, and Patrick J. Wolfe. "Testing for periodicity in speech waveforms." Journal of the Acoustical Society of America 125, no. 4 (April 2009): 2699. http://dx.doi.org/10.1121/1.4784326.

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Terzopoulos, D. "Co-occurrence analysis of speech waveforms." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 1 (February 1985): 5–30. http://dx.doi.org/10.1109/tassp.1985.1164511.

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Dissertations / Theses on the topic "Speech waveforms"

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Lowry, Andrew. "Efficient structures for vector quantisation of speech waveforms." Thesis, Queen's University Belfast, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292596.

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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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Deivard, Johannes. "How accuracy of estimated glottal flow waveforms affects spoofed speech detection performance." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48414.

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In the domain of automatic speaker verification, one of the challenges is to keep the malevolent people out of the system. One way to do this is to create algorithms that are supposed to detect spoofed speech. There are several types of spoofed speech and several ways to detect them, one of which is to look at the glottal flow waveform (GFW) of a speech signal. This waveform is often estimated using glottal inverse filtering (GIF), since, in order to create the ground truth GFW, special invasive equipment is required. To the author’s knowledge, no research has been done where the correlation of GFW accuracy and spoofed speech detection (SSD) performance is investigated. This thesis tries to find out if the aforementioned correlation exists or not. First, the performance of different GIF methods is evaluated, then simple SSD machine learning (ML) models are trained and evaluated based on their macro average precision. The ML models use different datasets composed of parametrized GFWs estimated with the GIF methods from the previous step. Results from the previous tasks are then combined in order to spot any correlations. The evaluations of the different methods showed that they created GFWs of varying accuracy. The different machine learning models also showed varying performance depending on what type of dataset that was being used. However, when combining the results, no obvious correlations between GFW accuracy and SSD performance were detected. This suggests that the overall accuracy of a GFW is not a substantial factor in the performance of machine learning-based SSD algorithms.

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Bamini, Praveen Kumar. "FPGA-based Implementation of Concatenative Speech Synthesis Algorithm." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000187.

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Choy, Eddie L. T. "Waveform interpolation speech coder at 4 kbs." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=20901.

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Speech coding at bit rates near 4 kbps is expected to be widely deployed in applications such as visual telephony, mobile and personal communications. This research focuses on developing a speech coder based on the waveform interpolation (WI) scheme, with an attempt to deliver near toll-quality speech at rates around 4 kbps. A WI coder has been simulated in floating-point using the C programming language. The high performance of the WI model has been confirmed by subjective listening tests in which the unquantized coder outperforms the 32 kbps G.726 standard (ADPCM) 98% of the time under clean input speech conditions; the reconstructed speech is perceived to be essentially indistinguishable from the original. When fully quantized, the speech quality of the WI coder at 4.25 kbps has been judged to be equivalent to or better than that of G.729 (the ITU-T toll-quality 8 kbps standard) for 45% of the test sentences. Further refinements of the quantization techniques are warranted to bring the coder closer to the toll-quality benchmark. Yet, the existing implementation has produced good quality coded speech with a high degree of intelligibility and naturalness when compared to the conventional coding schemes operating in the neighbourhood of 4 kbps.

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Leong, Michael. "Representing voiced speech using prototype waveform interpolation for low-rate speech coding." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56796.

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In recent years, research in narrow-band digital speech coding has achieved good quality speech coders at low rates of 4.8 to 8.0 kb/s. This thesis examines the method proposed by W. B. Kleijn called prototype waveform interpolation (PWI) for coding the voiced sections of speech efficiently to achieve a coder below 4.8 kb/s while maintaining, even improving, the perceptual quality of current coders.
In examining the PWI method, it was found that although the method generally works very well there are occasional sections of the reconstructed voiced speech where audible distortion can be heard, even when the prototypes are not quantized. The research undertaken in this thesis focuses on the fundamental principles behind modelling voiced speech using PWI instead of focusing on bit allocation for encoding the prototypes. Problems in the PWI method are found that may be have been overlooked as encoding error if full encoding were implemented.
Kleijn uses PWI to represent voiced sections of the excitation signal which is the residual obtained after the removal of short-term redundancies by a linear predictive filter. The problem with this method is that when the PWI reconstructed excitation is passed through the inverse filter to synthesize the speech undesired effects occur due to the time-varying nature of the filter. The reconstructed speech may have undesired envelope variations which result in audible warble.
This thesis proposes an energy fixup to smoothen the synthesized speech envelope when the interpolation procedure fails to provide the smooth linear result that is desired. Further investigation, however, leads to the final proposal in this thesis that PWI should he performed on the clean speech signal instead of the excitation to achieve consistently reliable results for all voiced frames.

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Choy, Eddie L. T. "Waveform interpolation speech coder at 4 kb/s." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0028/MQ50596.pdf.

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Davis, Andrew J. "Waveform coding of speech and voiceband data signals." Thesis, University of Liverpool, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232946.

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Eide, Ellen Marie. "A linguistic feature representation of the speech waveform." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12510.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
Includes bibliographical references (leaves 95-97).
by Ellen Marie Eide.
Ph.D.

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Zeghidour, Neil. "Learning representations of speech from the raw waveform." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE004/document.

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Bien que les réseaux de neurones soient à présent utilisés dans la quasi-totalité des composants d’un système de reconnaissance de la parole, du modèle acoustique au modèle de langue, l’entrée de ces systèmes reste une représentation analytique et fixée de la parole dans le domaine temps-fréquence, telle que les mel-filterbanks. Cela se distingue de la vision par ordinateur, un domaine où les réseaux de neurones prennent en entrée les pixels bruts. Les mel-filterbanks sont le produit d’une connaissance précieuse et documentée du système auditif humain, ainsi que du traitement du signal, et sont utilisées dans les systèmes de reconnaissance de la parole les plus en pointe, systèmes qui rivalisent désormais avec les humains dans certaines conditions. Cependant, les mel-filterbanks, comme toute représentation fixée, sont fondamentalement limitées par le fait qu’elles ne soient pas affinées par apprentissage pour la tâche considérée. Nous formulons l’hypothèse qu’apprendre ces représentations de bas niveau de la parole, conjontement avec le modèle, permettrait de faire avancer davantage l’état de l’art. Nous explorons tout d’abord des approches d’apprentissage faiblement supervisé et montrons que nous pouvons entraîner un unique réseau de neurones à séparer l’information phonétique de celle du locuteur à partir de descripteurs spectraux ou du signal brut et que ces représentations se transfèrent à travers les langues. De plus, apprendre à partir du signal brut produit des représentations du locuteur significativement meilleures que celles d’un modèle entraîné sur des mel-filterbanks. Ces résultats encourageants nous mènent par la suite à développer une alternative aux mel-filterbanks qui peut être entraînée à partir des données. Dans la seconde partie de cette thèse, nous proposons les Time-Domain filterbanks, une architecture neuronale légère prenant en entrée la forme d’onde, dont on peut initialiser les poids pour répliquer les mel-filterbanks et qui peut, par la suite, être entraînée par rétro-propagation avec le reste du réseau de neurones. Au cours d’expériences systématiques et approfondies, nous montrons que les Time-Domain filterbanks surclassent systématiquement les melfilterbanks, et peuvent être intégrées dans le premier système de reconnaissance de la parole purement convolutif et entraîné à partir du signal brut, qui constitue actuellement un nouvel état de l’art. Les descripteurs fixes étant également utilisés pour des tâches de classification non-linguistique, pour lesquelles elles sont d’autant moins optimales, nous entraînons un système de détection de dysarthrie à partir du signal brut, qui surclasse significativement un système équivalent entraîné sur des mel-filterbanks ou sur des descripteurs de bas niveau. Enfin, nous concluons cette thèse en expliquant en quoi nos contributions s’inscrivent dans une transition plus large vers des systèmes de compréhension du son qui pourront être appris de bout en bout
While deep neural networks are now used in almost every component of a speech recognition system, from acoustic to language modeling, the input to such systems are still fixed, handcrafted, spectral features such as mel-filterbanks. This contrasts with computer vision, in which a deep neural network is now trained on raw pixels. Mel-filterbanks contain valuable and documented prior knowledge from human auditory perception as well as signal processing, and are the input to state-of-the-art speech recognition systems that are now on par with human performance in certain conditions. However, mel-filterbanks, as any fixed representation, are inherently limited by the fact that they are not fine-tuned for the task at hand. We hypothesize that learning the low-level representation of speech with the rest of the model, rather than using fixed features, could push the state-of-the art even further. We first explore a weakly-supervised setting and show that a single neural network can learn to separate phonetic information and speaker identity from mel-filterbanks or the raw waveform, and that these representations are robust across languages. Moreover, learning from the raw waveform provides significantly better speaker embeddings than learning from mel-filterbanks. These encouraging results lead us to develop a learnable alternative to mel-filterbanks, that can be directly used in replacement of these features. In the second part of this thesis we introduce Time-Domain filterbanks, a lightweight neural network that takes the waveform as input, can be initialized as an approximation of mel-filterbanks, and then learned with the rest of the neural architecture. Across extensive and systematic experiments, we show that Time-Domain filterbanks consistently outperform melfilterbanks and can be integrated into a new state-of-the-art speech recognition system, trained directly from the raw audio signal. Fixed speech features being also used for non-linguistic classification tasks for which they are even less optimal, we perform dysarthria detection from the waveform with Time-Domain filterbanks and show that it significantly improves over mel-filterbanks or low-level descriptors. Finally, we discuss how our contributions fall within a broader shift towards fully learnable audio understanding systems

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Books on the topic "Speech waveforms"

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Yaghmaie, Khashayar. Prototype waveform interpolation based low bit rate speech coding. 1997.

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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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Dutoit, Thierry, and Yannis Stylianou. Text-to-Speech Synthesis. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0017.

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This article gives an introduction to state-of-the-art text-to-speech (TTS) synthesis systems, showing both the natural language processing and the digital signal processing problems involved. Text-to-speech (TTS) synthesis is the art of designing talking machines. The article begins with brief user-oriented description of a general TTS system and comments on its commercial applications. It then gives a functional diagram of a modern TTS system, highlighting its components. It describes its morphosyntactic module. Furthermore, it examines why sentence-level phonetization cannot be achieved by a sequence of dictionary look-ups, and describes possible implementations of the phonetizer. Finally, the article describes prosody generation, outlining how intonation and duration can approximately be computed from text. Prosody refers to certain properties of the speech signal, which are related to audible changes in pitch, loudness, and syllable length. This article also introduces the two main existing categories of techniques for waveform generation: synthesis by rule and concatenative synthesis.

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

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Book chapters on the topic "Speech waveforms"

1

Kittler, J., and A. E. Lucas. "A New Method for Dynamic Time Alignment of Speech Waveforms." In Speech Recognition and Understanding, 537–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76626-8_53.

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Sinha, Priyabrata. "Waveform Coders." In Speech Processing in Embedded Systems, 101–12. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_7.

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Haagen, Jesper, and W. Bastiaan Kleijn. "Waveform Interpolation." In Modern Methods of Speech Processing, 75–99. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2281-2_4.

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Kleijn, W. Bastiaan, and Wolfgang Granzow. "Waveform Interpolation in Speech Coding." In Speech and Audio Coding for Wireless and Network Applications, 111–18. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3232-3_15.

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Tóth, Bálint Pál, Kornél István Kis, György Szaszák, and Géza Németh. "Ensemble Deep Neural Network Based Waveform-Driven Stress Model for Speech Synthesis." In Speech and Computer, 271–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_32.

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de Mori, R. "Working Group B: Waveform and Speech Recognition." In Syntactic and Structural Pattern Recognition, 447–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83462-2_27.

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Weiner, A. M., P. S. D. Lin, and R. B. Marcus. "Photoemissive Testing of High-Speed Electrical Waveforms." In Picosecond Electronics and Optoelectronics II, 56–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72970-6_12.

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Paul, Subham, and Debashis Chakraborty. "Efficient Speech Compression Using Waveform Coding in Time Domain." In Emerging Research in Computing, Information, Communication and Applications, 415–30. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4741-1_37.

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Smidi, Ghaya, Aicha Bouzid, and Noureddine Ellouze. "Glottal Closure Instant Detection by the Multi-scale Product of the Derivative Glottal Waveform Signal." In Recent Advances in Nonlinear Speech Processing, 191–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28109-4_19.

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Panda, Soumya Priyadarsini, and Ajit Kumar Nayak. "Spectral Smoothening Based Waveform Concatenation Technique for Speech Quality Enhancement in Text-to-Speech Systems." In Advances in Intelligent Systems and Computing, 425–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_36.

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Conference papers on the topic "Speech waveforms"

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Hoshen, Yedid, Ron J. Weiss, and Kevin W. Wilson. "Speech acoustic modeling from raw multichannel waveforms." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178847.

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Cvetkovic, Z. "Modulating waveforms for OFDM." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.760629.

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Tokuday, Keiichi, and Heiga Zen. "Directly modeling speech waveforms by neural networks for statistical parametric speech synthesis." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178765.

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Cvetkovic, Beferull-Lozano, and Buja. "Robust phoneme discrimination using acoustic waveforms." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005740.

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Sujatha, B. K., P. S. Satyanarayana, and K. N. Haribhat. "Digital Coding of Speech Waveforms Using the Proposed ADM." In 2008 Second International Conference on Future Generation Communication and Networking (FGCN). IEEE, 2008. http://dx.doi.org/10.1109/fgcn.2008.160.

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Mporas, Iosif, Todor Ganchev, and Nikos Fakotakis. "A hybrid architecture for automatic segmentation of speech waveforms." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518645.

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Fainberg, Joachim, Ondrej Klejch, Erfan Loweimi, Peter Bell, and Steve Renals. "Acoustic Model Adaptation from Raw Waveforms with Sincnet." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9003974.

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Hongbing Zhang, Fan, and Lindsey. "Wavelet packet waveforms for multicarrier CDMA communications." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005207.

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Lourey, Simon J. "Frequency hopping waveforms for continuous active sonar." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178287.

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Dai, Wei, Chia Dai, Shuhui Qu, Juncheng Li, and Samarjit Das. "Very deep convolutional neural networks for raw waveforms." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952190.

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Reports on the topic "Speech waveforms"

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Nehl, Albert. Investigation of techniques for high speed CMOS arbitrary waveform generation. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5993.

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Haller, Gunther. High-Speed, High-Resolution Analog Waveform Sampling in VLSI Technology. Office of Scientific and Technical Information (OSTI), October 1998. http://dx.doi.org/10.2172/10132.

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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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