Relevant bibliographies by topics / Speech filtering

Academic literature on the topic 'Speech filtering'

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Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Speech filtering"

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Clarkson, P. M., P. R. White, and J. A. Mardell. "Adaptive filtering for speech enhancement." Journal of the Acoustical Society of America 80, S1 (December 1986): S20. http://dx.doi.org/10.1121/1.2023697.

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Wen-Rong Wu and Po-Cheng Chen. "Subband Kalman filtering for speech enhancement." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 45, no. 8 (1998): 1072–83. http://dx.doi.org/10.1109/82.718814.

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O'Shaughnessy, D. "Speech enhancement by selective spectral filtering." Journal of the Acoustical Society of America 87, S1 (May 1990): S104. http://dx.doi.org/10.1121/1.2027800.

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Zheng, Chengshi, Zheng-Hua Tan, Renhua Peng, and Xiaodong Li. "Guided spectrogram filtering for speech dereverberation." Applied Acoustics 134 (May 2018): 154–59. http://dx.doi.org/10.1016/j.apacoust.2017.11.016.

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Wang, Jie, Linhuang Yan, Qiaohe Yang, and Minmin Yuan. "Speech enhancement based on perceptually motivated guided spectrogram filtering." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 5443–54. http://dx.doi.org/10.3233/jifs-202278.

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In this paper, a single-channel speech enhancement algorithm is proposed by using guided spectrogram filtering based on masking properties of human auditory system when considering a speech spectrogram as an image. Guided filtering is capable of sharpening details and estimating unwanted textures or background noise from the noisy speech spectrogram. If we consider the noisy spectrogram as a degraded image, we can estimate the spectrogram of the clean speech signal using guided filtering after subtracting noise components. Combined with masking properties of human auditory system, the proposed algorithm adaptively adjusts and reduces the residual noise of the enhanced speech spectrogram according to the corresponding masking threshold. Because the filtering output is a local linear transform of the guidance spectrogram, the local mask window slides can be efficiently implemented via box filter with O(N) computational complexity. Experimental results show that the proposed algorithm can effectively suppress noise in different noisy environments and thus can greatly improve speech quality and speech intelligibility.
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Jarvinen, Kari J. "Digital coding of speech signals using analysis filtering and synthesis filtering." Journal of the Acoustical Society of America 102, no. 3 (September 1997): 1283. http://dx.doi.org/10.1121/1.420024.

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Cheng, Chong, and Li Huang. "Research on Speech Enhancement Based on Wiener Filtering." Applied Mechanics and Materials 513-517 (February 2014): 3130–33. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3130.

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eech enhancement based on Wiener filtering has good noise robustness, and it is efficient and easy-to-implement. In this paper, Wiener filtering and its modified form, Iterative Wiener Filtering are demonstrated. Then, their respective advantages and disadvantages are outlined. Finally, the application field and location of each method are also pointed out.
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Espy-Wilson, Carol Y., Venkatesh R. Chari, Joel M. MacAuslan, Caroline B. Huang, and Michael J. Walsh. "Enhancement of Electrolaryngeal Speech by Adaptive Filtering." Journal of Speech, Language, and Hearing Research 41, no. 6 (December 1998): 1253–64. http://dx.doi.org/10.1044/jslhr.4106.1253.

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Artificial larynges provide a means of verbal communication for people who have either lost or are otherwise unable to use their larynges. Although they enable adequate communication, the resulting speech has an unnatural quality and is significantly less intelligible than normal speech. One of the major problems with the widely used Transcutaneous Artificial Larynx (TAL) is the presence of a steady background noise caused by the leakage of acoustic energy from the TAL, its interface with the neck, and the surrounding neck tissue. The severity of the problem varies from speaker to speaker, partly depending upon the characteristics of the individual's neck tissue. The present study tests the hypothesis that TAL speech is enhanced in quality (as assessed through listener preference judgments) and intelligibility by removal of the inherent, directly radiated background signal. In particular, the focus is on the improvement of speech over the telephone or through some other electronic communication medium. A novel adaptive filtering architecture was designed and implemented to remove the background noise. Perceptual tests were conducted to assess speech, from two individuals with a laryngectomy and two normal speakers using the Servox TAL, before and after processing by the adaptive filter. A spectral analysis of the adaptively filtered TAL speech revealed a significant reduction in the amount of background source radiation yet preserved the acoustic characteristics of the vocal output. Results from the perceptual tests indicate a clear preference for the processed speech. In general, there was no significant improvement or degradation in intelligibility. However, the processing did improve the intelligibility of word-initial non-nasal consonants.
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Cho, Young-Im, and Sung-Soon Jang. "Implementation of Speech Recognition Filtering at Emergency." Journal of Korean Institute of Intelligent Systems 20, no. 2 (April 25, 2010): 208–13. http://dx.doi.org/10.5391/jkiis.2010.20.2.208.

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Roth, Robert. "Lexical tree pre-filtering in speech recognition." Journal of the Acoustical Society of America 107, no. 3 (2000): 1090. http://dx.doi.org/10.1121/1.428395.

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Dissertations / Theses on the topic "Speech filtering"

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Ledoux, Christelle Michelle. "Robust speech filtering in impulsive noise environments." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/46325.

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This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by means of a robust nonlinear estimator known as the Schweppe-type Huber GM-estimator. Simulation results are presented that demonstrate the effectiveness of the filter. Another contribution of the work is the development of a robust version of the Kalman filter based on the Huber M-estimator. The performances of this filter are evaluated for a simple autoregressive process.
Master of Science
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Ramachandran, Ravi P. "Pitch filtering in adaptive predictive coding of speech." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=65345.

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Klein, Mark 1977. "Signal subspace speech enhancement with perceptual post-filtering." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33975.

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Speech enhancement blocks form a critical part of voice communications systems. Unfortunately, most enhancement schemes have difficulty eliminating noise from speech without introducing distortion or artefacts. Many of the disturbances originate from poor parameter estimation and interframe fluctuations.
This thesis introduces the Enhanced Signal Subspace (ESS) system to mitigate the above problems. Based on a signal subspace framework, ESS has been designed to attenuate disturbances while minimizing audible distortion.
Artefacts are reduced by employing an auditory post-filter to smooth the enhanced speech spectra. This filter performs averaging in a manner that exploits the properties of the human auditory system. As such, distortion of the underlying speech signal is reduced.
Testing shows that listeners prefer the proposed algorithm to traditional signal subspace speech enhancement.
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Chan, Dominic Sai Fan. "Speech production modelling based on glottal inverse filtering." Thesis, Imperial College London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307161.

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Lewine, Andrew (Andrew P. ). "Speech filtering for improving intelligibility in noisy transients." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66433.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Hearing impairment is a problem that affects a large percentage of the population. Cochlear implants allow those with profound or total hearing loss to regain some hearing by stimulating auditory nerve fibers with implanted electrodes, in response to sound picked up by an external microphone. The signal processing chain from microphone input to stimulation output is an important factor in the overall speech intelligibility of the implant system. This thesis work improves on an existing ultra-low-power cochlear implant system by utilizing an improved noise and power efficient bandpass filter bank to implement a novel frequency-selective gain control algorithm capable of reducing, and in some cases removing, loud transient noises, thereby improving speech intelligibility. This gain control algorithm takes advantage of the inherent frequency-specific gain control afforded by the improved bandpass filter topology. This contribution makes an improvement to the existing state-of-the-art system in both power efficiency and performance.
by Andrew Lewine.
M.Eng.
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Dubbin, Gregory. "Applying particle filtering to unsupervised part-of-speech induction." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:48caedb6-478f-4bb0-8ca7-975ee7fe5e38.

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Statistical Natural Language Processing (NLP) lies at the intersection of Computational Linguistics and Machine Learning. As linguistic models incorporate more subtle nuances of language and its structure, standard inference techniques can fall behind. One such application is research on the unsupervised induction of part-of-speech tags. It has the potential to improve both our understanding of the plausibility of theories of first language acquisition, and Natural Language Processing applications such as Speech Recognition and Machine Translation. Sequential Monte Carlo (SMC) approaches, i.e. particle filters, are well suited to approximating such models. This thesis seeks to determine whether one application of SMC methods, particle Gibbs sampling, is capable of performing inference in otherwise intractable NLP applications. Specifically, this research analyses the benefits and drawbacks to relying on particle Gibbs to perform unsupervised part-of-speech induction without the flawed one-tag-per-type assumption of similar approaches. Additionally, this thesis explores the affects of type-based supervision with tag-dictionaries extracted from annotated corpora or from the wiktionary. The semi-supervised tag dictionary improves the performance of the local Gibbs PYP-HMM sampler enough to nearly match the performance of the particle Gibbs type-sampler. Finally, this thesis also extends the Pitman-Yor HMM tagger of Blunsom and Cohn (2011) to include an explicit model of the lexicon which encodes those tags from which a word-type may be generated. This has the effect of both biasing the model to produce fewer tags per type and modelling the tendency for open class words to be ambiguous between only a subset of the available tags. Furthermore, I extend the type based particle Gibbs inference algorithm to simultaneously resample the ambiguity class as well as tags for all of the tokens of a given word type. The result is a principled probabilistic model of part-of-speech induction that achieves state-of-the-art performance. Overall, the experiments and contributions of this thesis demonstrate the applicability of the particle Gibbs sampler and particle methods in general to otherwise intractable problems in NLP.
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Papanagiotou, Kyriakos. "Enhancement of body conducted speech from an ear microphone." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289914.

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Darlington, David J. "The enhancement of noise-corrupted speech by sub-band adaptive filtering." Thesis, University of the West of Scotland, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388213.

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Hu, Rong. "Enhancement of adaptive de-correlation filtering separation model for robust speech recognition." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4682.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 25, 2007) Vita. Includes bibliographical references.
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Mustiere, Frederic. "Particle filtering methods for the enhancement of speech corrupted by additive noise." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27398.

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In this work, we study the application of particle filtering (PF) algorithms to the problem of speech enhancement. The goal of the thesis is to devise PF algorithms that will enhance speech signals corrupted by additive noise, and to evaluate their performance via comparisons with other existing algorithms based on several quality measures. Speech enhancement, or noise reduction, is an important problem in many applications, such as telephony and telecommunications in general, sound recording, human-coaching interface (where speech recognition is important), etc. Even though many algorithms already exist for speech enhancement, there is still very much work to do, especially in terms of intelligibility. In many cases, it may be easier to understand the original, noisy speech rather than the processed, "cleaned-out" one. In other cases, the residual noise may be too annoying to carry out a comfortable conversation. In this context, new approaches for the denoising of speech are welcome. As a first contribution, a practical approach to deriving simple Rao-Blackwellised Particle Filters (RBPFs), which was developed in parallel with a theoretic review of PFs, is presented. In addition, a novel algorithm, called the modified Rao-Blackwellised Particle Filter (RBPF), is proposed to reduce the computational load of regular RBPFs. Several new speech enhancement methods using particle filters are also derived, and shown to outperform some other existing PF-based algorithms. Accessorily, a novel strategy to extend their range of application to colored noise is explained and applied. Comparatively to the other types of enhancement algorithms tested (including spectral subtraction, signal subspace, dual extended Kalman filter, perceptually constrained Kalman filter, dual perceptually constrained unscented Kalman filter) we find that the particle-filter based algorithms presented have the advantage of not introducing any musical noise. Furthermore, in the conditions of our experiments, using several objective measures we find that they are able to compete with and outperform most of the other algorithms tested. Using these measures and based on informal listening, we highlight their advantages---naturalness of the enhanced speech, low intrusiveness of the non-musical residual noise, very good performance at high SNR, flexibility---and their main limitations---intraspeech residual noise "modulated" by the speech, computational burden. Considering how flexible and parametrizable PFs are, there is a strong potential for further improvement.
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Books on the topic "Speech filtering"

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Abel, Andrew, and Amir Hussain. Cognitively Inspired Audiovisual Speech Filtering. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13509-0.

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Vega, Leonardo Rey. A Rapid Introduction to Adaptive Filtering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Mathai, Sybil. Factors involved in the comprehension of speech: Speech rate, filtering, working memory and reading comprehension. Sudbury, Ont: Laurentian University, Department of Psychology, 1997.

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Diniz, Paulo S. R. Adaptive Filtering: Algorithms and Practical Implementation. 4th ed. Boston, MA: Springer US, 2013.

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Shen, Bo. Nonlinear Stochastic Systems with Incomplete Information: Filtering and Control. London: Springer London, 2013.

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Boulanger, Richard Charles. The transformation of speech into music: A musical exploration and interpretation of two recent digital filtering techniques. San Diego: [s.n.], 1985.

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Chen, Jongdon. Optimal Filtering for Speech Enhancement (Synthesis Lectures on Speech and Audio Processing). Morgan & Claypool Publishers, 2007.

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Diniz, Paulo S. R. Adaptive Filtering. Springer, 2012.

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Rey, Hernan, and Leonardo Rey Vega. A Rapid Introduction to Adaptive Filtering. Springer, 2012.

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Diniz, Paulo S. R. Adaptive Filtering: Algorithms and Practical Implementation. Springer, 2012.

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Book chapters on the topic "Speech filtering"

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Markovich-Golan, Shmulik, Walter Kellermann, and Sharon Gannot. "Spatial Filtering." In Audio Source Separation and Speech Enhancement, 189–217. Chichester, UK: John Wiley & Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119279860.ch10.

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Aleinik, Sergei. "Optimization of Zelinski Post-filtering Calculation." In Speech and Computer, 523–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_63.

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Sainath, Tara N., Dimitri Kanevsky, David Nahamoo, Bhuvana Ramabhadran, and Stephen Wright. "Sparse Representations for Speech Recognition." In Compressed Sensing & Sparse Filtering, 455–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38398-4_15.

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Abel, Andrew, and Amir Hussain. "Audio and Visual Speech Relationship." In Cognitively Inspired Audiovisual Speech Filtering, 5–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13509-0_2.

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Kedem, Benjamin, and Konstantinos Fokianos. "Semiparametric Filtering in Speech Processing." In Mathematical Foundations of Speech and Language Processing, 271–81. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4419-9017-4_12.

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Gerkmann, Timo, and Emmanuel Vincent. "Spectral Masking and Filtering." In Audio Source Separation and Speech Enhancement, 65–85. Chichester, UK: John Wiley & Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119279860.ch5.

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Abel, Andrew, and Amir Hussain. "Introduction." In Cognitively Inspired Audiovisual Speech Filtering, 1–4. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13509-0_1.

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Abel, Andrew, and Amir Hussain. "The Research Context." In Cognitively Inspired Audiovisual Speech Filtering, 13–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13509-0_3.

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Abel, Andrew, and Amir Hussain. "A Two Stage Multimodal Speech Enhancement System." In Cognitively Inspired Audiovisual Speech Filtering, 35–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13509-0_4.

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Abel, Andrew, and Amir Hussain. "Experiments, Results, and Analysis." In Cognitively Inspired Audiovisual Speech Filtering, 53–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13509-0_5.

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Conference papers on the topic "Speech filtering"

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Qin Li and Les Atlas. "Coherent modulation filtering for speech." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518651.

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Mack, Wolfgang, and Emanuel A. P. Habets. "Declipping Speech Using Deep Filtering." In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2019. http://dx.doi.org/10.1109/waspaa.2019.8937287.

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Xue, Wei, Alastair H. Moore, Mike Brookes, and Patrick A. Naylor. "Multichannel Kalman Filtering for Speech Ehnancement." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461903.

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Crisafulli, S., J. D. Mills, and R. R. Bitmead. "Kalman filtering techniques in speech coding." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.225968.

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Abdollahy, A., and M. Geravanchizadeh. "Speech enhancement using combinational adaptive filtering." In 2008 International Symposium on Telecommunications (IST). IEEE, 2008. http://dx.doi.org/10.1109/istel.2008.4651404.

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Jaramillo, Alfredo Esquivel, Jesper Kjrer Nielsen, and Mads GnesbOll Christensen. "On Optimal Filtering for Speech Decomposition." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553512.

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Xue, Wei, Gang Quan, Chao Zhang, Guohong Ding, Xiaodong He, and Bowen Zhou. "Neural Kalman Filtering for Speech Enhancement." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413499.

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Aysal, Tuncer C., and Kenneth E. Barner. "Robust Meridian Filtering." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366786.

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Djuric, Petar M., Ting Lu, and Monica F. Bugallo. "Multiple Particle Filtering." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367053.

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Fischer, Dorte, and Timo Gerkmann. "Single-microphone speech enhancement using MVDR filtering and Wiener post-filtering." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471665.

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Reports on the topic "Speech filtering"

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Zoltowski, Michael D. Space-Time Equalization for High-Speed Wireless Digital Communications Based on Multipath-Incorporating Matched Filtering, Zero Forcing Equalization, and MMSE. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416896.

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