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Journal articles on the topic 'Speech filtering'

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Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ta-Hsin Li and J. D. Gibson. "Speech analysis and segmentation by parametric filtering." IEEE Transactions on Speech and Audio Processing 4, no. 3 (May 1996): 203–13. http://dx.doi.org/10.1109/89.496216.

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12

Sreenivas, T. V., and P. Kirnapure. "Codebook constrained Wiener filtering for speech enhancement." IEEE Transactions on Speech and Audio Processing 4, no. 5 (1996): 383–89. http://dx.doi.org/10.1109/89.536932.

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13

Tesch, Kristina, and Timo Gerkmann. "Nonlinear Spatial Filtering in Multichannel Speech Enhancement." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 1795–805. http://dx.doi.org/10.1109/taslp.2021.3076372.

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14

Wang, Yao Qi, Xiao Peng Wang, and Lv Cheng Wang. "Pitch Detection Method Based on Morphological Filtering." Applied Mechanics and Materials 596 (July 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.596.433.

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A new method of pitch detection based on morphological filtering is proposed. Noisy speech signal is filtered by morphological filtering to remove the noise and highlight pitch, and then HHT is employed to get Hilbert-Huang spectrum and to calculate instantaneous energy and its derivative. The moment of glottal opening and closing can be accurately located through tracking mutation of instantaneous energy, so that variation of pitch period can be accurately tracked. Compared with other traditional method of pitch detection, this method not only truly describes non-stationary and non-linear characteristics of speech signal, but also it is an adaptive process for the analysis of the speech signal. The experiments showed that the method has strong anti-noise and can accurately detect the pitch of speech in low SNR.

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15

Liu, Yu Hong, Dong Mei Zhou, and Jing Di. "An Improved Speech Enhancement Algorithm Based on Wiener-Filtering." Advanced Materials Research 989-994 (July 2014): 2565–68. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2565.

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This paper proposes an improved speech enhancement algorithm based on Wiener-Filtering, which addresses the problems of speech distortion and musical noise. The proposed algorithm adopts the masking properties of human auditory system on calculating the gain of spectrum point, in order that the signal in the enhanced speech whose energy is lower than the threshold will not be decreased further and the less distortion will be brought to enhanced speech by the trade-off between the noise elimination and speech signal distortion. What’s more, in order to eliminate the “musical noise”, a spectrum-shaping technology using averaging method between adjacent frames is adopted. And to guarantee the real-time application, two-stage moving-average strategy is adopted. The computer simulation results show that the proposed algorithm is superior to the traditional Wiener method in the low CPU cost, real-time statistics, the reduction of the speech distortion and residual musical noise.

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16

Zenton Goh, Kah-Chye Tan, and B. T. G. Tan. "Kalman-filtering speech enhancement method based on a voiced-unvoiced speech model." IEEE Transactions on Speech and Audio Processing 7, no. 5 (1999): 510–24. http://dx.doi.org/10.1109/89.784103.

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17

Li, Jin, and Kun Shen. "Applied-Information Technology with Speech Enhancement Based on EMD and MF." Advanced Materials Research 1046 (October 2014): 384–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.384.

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Aiming at traditional methods cannot get good performance in noisy environments, an improved method for speech enhancement based on Empirical Mode Decomposition (EMD) and Morphology Filtering (MF) was proposed. The method firstly uses EMD to obtain Intrinsic Mode Function (IMF) and for hard threshold processing, then selects appropriate structuring element to construct MF for filtering processing in remaining IMFs. Finally, speech enhancement signal is reconstructed for each IMFs. Experimental results show that the proposed method for speech enhancement has better de-noising effect by comparing time-domain waveform and spectrogram. Moreover, the quality of reconstructed speech enhancement signal has been significantly improved.

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18

Abd El-Fattah, M. A., Moawad Ibrahim Dessouky, Salah M. Diab, and Fathi El-Sayed Abd El-Samie. "SPEECH ENHANCEMENT USING AN ADAPTIVE WIENER FILTERING APPROACH." Progress In Electromagnetics Research M 4 (2008): 167–84. http://dx.doi.org/10.2528/pierm08061206.

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19

Hayder, Mohammad, and Dr Ahlam Mahmood. "An Optimized Adaptive Filtering for Speech Noise Cancellation." AL-Rafdain Engineering Journal (AREJ) 23, no. 5 (December 28, 2015): 43–53. http://dx.doi.org/10.33899/rengj.2015.108996.

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20

Wu, Chaogang, Bo Li, and Jin Zheng. "A Speech Enhancement Method Based on Kalman Filtering." International Journal of Wireless and Microwave Technologies 1, no. 2 (April 15, 2011): 55–61. http://dx.doi.org/10.5815/ijwmt.2011.02.08.

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21

Xue, Wei, Alastair H. Moore, Mike Brookes, and Patrick A. Naylor. "Modulation-Domain Multichannel Kalman Filtering for Speech Enhancement." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 10 (October 2018): 1833–47. http://dx.doi.org/10.1109/taslp.2018.2845665.

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22

Gobl, Christer, and James Mahshie. "Inverse Filtering of Nasalized Vowels Using Synthesized Speech." Journal of Voice 27, no. 2 (March 2013): 155–69. http://dx.doi.org/10.1016/j.jvoice.2012.09.004.

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23

Ki Yong Lee, Byung-Gook Lee, and Souguil Ann. "Adaptive filtering for speech enhancement in colored noise." IEEE Signal Processing Letters 4, no. 10 (October 1997): 277–79. http://dx.doi.org/10.1109/97.633767.

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24

Vimala.C, Vimala C., and Radha V. Radha.V. "Optimal Adaptive Filtering Technique for Tamil Speech Enhancement." International Journal of Computer Applications 41, no. 17 (March 31, 2012): 23–29. http://dx.doi.org/10.5120/5633-7996.

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25

Radfar, Mohammad H., and Richard M. Dansereau. "Single-Channel Speech Separation Using Soft Mask Filtering." IEEE Transactions on Audio, Speech and Language Processing 15, no. 8 (November 2007): 2299–310. http://dx.doi.org/10.1109/tasl.2007.904233.

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26

Raitio, Tuomo, Antti Suni, Junichi Yamagishi, Hannu Pulakka, Jani Nurminen, Martti Vainio, and Paavo Alku. "HMM-Based Speech Synthesis Utilizing Glottal Inverse Filtering." IEEE Transactions on Audio, Speech, and Language Processing 19, no. 1 (January 2011): 153–65. http://dx.doi.org/10.1109/tasl.2010.2045239.

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27

Kollmeier, B. "Speech Enhancement by Filtering in the Loudness Domain." Acta Oto-Laryngologica 109, sup469 (January 1, 1990): 207–14. http://dx.doi.org/10.1080/00016489.1990.12088431.

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28

Lin, L., W. H. Holmes, and E. Ambikairajah. "Speech denoising using perceptual modification of Wiener filtering." Electronics Letters 38, no. 23 (2002): 1486. http://dx.doi.org/10.1049/el:20020965.

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29

ZHU, Q. "Noise-Robust Speech Analysis Using Running Spectrum Filtering." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 2 (February 1, 2005): 541–48. http://dx.doi.org/10.1093/ietfec/e88-a.2.541.

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30

Zoghlami, Novlene, and Zied Lachiri. "Application of Perceptual Filtering Models to Noisy Speech Signals Enhancement." Journal of Electrical and Computer Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/282019.

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This paper describes a new speech enhancement approach using perceptually based noise reduction. The proposed approach is based on the application of two perceptual filtering models to noisy speech signals: the gammatone and the gammachirp filter banks with nonlinear resolution according to the equivalent rectangular bandwidth (ERB) scale. The perceptual filtering gives a number of subbands that are individually spectral weighted and modified according to two different noise suppression rules. The importance of an accurate noise estimate is related to the reduction of the musical noise artifacts in the processed speech that appears after classic subtractive process. In this context, we use continuous noise estimation algorithms. The performance of the proposed approach is evaluated on speech signals corrupted by real-world noises. Using objective tests based on the perceptual quality PESQ score and the quality rating of signal distortion (SIG), noise distortion (BAK) and overall quality (OVRL), and subjective test based on the quality rating of automatic speech recognition (ASR), we demonstrate that our speech enhancement approach using filter banks modeling the human auditory system outperforms the conventional spectral modification algorithms to improve quality and intelligibility of the enhanced speech signal.

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31

Oh, Joon-Yeoul, and Rick A. Aukerman. "Freedom Of Speech And Censorship In The Internet." International Journal of Management & Information Systems (IJMIS) 17, no. 4 (September 29, 2013): 251. http://dx.doi.org/10.19030/ijmis.v17i4.8101.

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Internet censorship or internet content filtering is used to protect people from harmful materials, such as child pornography, as well as defamation and fraud, which are easily perpetrated on the internet. However, implementing censorship creates technical and social issues, such as over-blocking or false detection, decreased network performance, and freedom of speech. This paper describes internet content filtering approaches and technical difficulties to implementation. This paper also discusses censorship and freedom of speech with actual examples.

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32

Yu, Hongjiang, Wei-Ping Zhu, Zhiheng Ouyang, and Benoit Champagne. "A hybrid speech enhancement system with DNN based speech reconstruction and Kalman filtering." Multimedia Tools and Applications 79, no. 43-44 (August 29, 2020): 32643–63. http://dx.doi.org/10.1007/s11042-020-09563-5.

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33

Fritzell, Bjorn. "Inverse filtering." Journal of Voice 6, no. 2 (January 1992): 111–14. http://dx.doi.org/10.1016/s0892-1997(05)80124-9.

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34

Wang, Jie, Linhuang Yan, Jiayi Tian, and Minmin Yuan. "Speech enhancement algorithm of improved OMLSA based on bilateral spectrogram filtering." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6881–89. http://dx.doi.org/10.3233/jifs-192088.

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In this paper, a bilateral spectrogram filtering (BSF)-based optimally modified log-spectral amplitude (OMLSA) estimator for single-channel speech enhancement is proposed, which can significantly improve the performance of OMLSA, especially in highly non-stationary noise environments, by taking advantage of bilateral filtering (BF), a widely used technology in image and visual processing, to preprocess the spectrogram of the noisy speech. BSF is capable of not only sharpening details, removing unwanted textures or background noise from the noisy speech spectrogram, but also preserving edges when considering a speech spectrogram as an image. The a posteriori signal-to-noise ratio (SNR) of OMLSA algorithm is estimated after applying BSF to the noisy speech. Besides, in order to reduce computing costs, a fast and accurate BF is adopted to reduce the algorithm complexity O(1) for each time-frequency bin. Finally, the proposed algorithm is compared with the original OMLSA and other classic denoising methods using various types of noise with different signal-to-noise ratios in terms of objective evaluation metrics such as segmental signal-to-noise ratio improvement and perceptual evaluation of speech quality. The results show the validity of the improved BSF-based OMLSA algorithm.

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35

Loven, Faith C., and M. Jane Collins. "Reverberation, Masking, Filtering, and Level Effects on Speech Recognition Performance." Journal of Speech, Language, and Hearing Research 31, no. 4 (December 1988): 681–95. http://dx.doi.org/10.1044/jshr.3104.681.

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The purpose of this investigation was to describe the interactive effects of four signal modifications typically encountered in everyday communication settings. These modifications included reverberation, masking, filtering, and fluctuation in speech intensity. The relationship between recognition performance and spectral changes to the speech signal due to the presence of these signal alterations was also studied. The interactive effects of these modifications were evaluated by obtaining indices of nonsense syllable recognition ability from normally hearing listeners for systematically varied combinations of the four signal parameters. The results of this study were in agreement with previous studies concerned with the effect of these variables in isolation on speech recognition ability. When present in combination, the direction of each variable's effect on recognition performance is maintained; however, the magnitude of the effect increases. The results of this investigation are reasonably accounted for by a spectral theory of speech recognition.

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36

Stenzel, Sebastian, and Jürgen Freudenberger. "Blind-Matched Filtering for Speech Enhancement with Distributed Microphones." Journal of Electrical and Computer Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/169853.

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A multichannel noise reduction and equalization approach for distributed microphones is presented. The speech enhancement is based on a blind-matched filtering algorithm that combines the microphone signals such that the output SNR is maximized. The algorithm is developed for spatially uncorrelated but nonuniform noise fields, that is, the noise signals at the different microphones are uncorrelated, but the noise power spectral densities can vary. However, no assumptions on the array geometry are made. The proposed method will be compared to the speech distortion-weighted multichannel Wiener filter (SDW-MWF). Similar to the SDW-MWF, the new algorithm requires only estimates of the input signal to noise ratios and the input cross-correlations. Hence, no explicit channel knowledge is necessary. A new version of the SDW-MWF for spatially uncorrelated noise is developed which has a reduced computational complexity, because matrix inversions can be omitted. The presented blind-matched filtering approach is similar to this SDW-MWF for spatially uncorrelated noise but additionally achieves some improvements in the speech quality due to a partial equalization of the acoustic system.

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37

Shen, Guanghu, Soo-Young Suk, and Hyun-Yeol Chung. "Improved feature enhancement using temporal filtering in speech recognition." IEICE Electronics Express 7, no. 15 (2010): 1099–105. http://dx.doi.org/10.1587/elex.7.1099.

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38

Jung, Ho-Young. "Filtering of Filter-Bank Energies for Robust Speech Recognition." ETRI Journal 26, no. 3 (June 15, 2004): 273–76. http://dx.doi.org/10.4218/etrij.04.0203.0033.

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39

Tu, Jingxian, and Youshen Xia. "Effective Kalman filtering algorithm for distributed multichannel speech enhancement." Neurocomputing 275 (January 2018): 144–54. http://dx.doi.org/10.1016/j.neucom.2017.05.048.

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40

Wang, Jing, Xiang Xie, and Jingming Kuang. "Microphone array speech enhancement based on tensor filtering methods." China Communications 15, no. 4 (April 2018): 141–52. http://dx.doi.org/10.1109/cc.2018.8357692.

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41

Story, Brad H., and Kate Bunton. "Formant measurement in children’s speech based on spectral filtering." Speech Communication 76 (February 2016): 93–111. http://dx.doi.org/10.1016/j.specom.2015.11.001.

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42

Tyagi, Vivek, Christian Wellekens, and Dirk T. M. Slock. "Least squares filtering of speech signals for robust ASR." Speech Communication 48, no. 11 (November 2006): 1528–44. http://dx.doi.org/10.1016/j.specom.2006.07.010.

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43

So, Stephen, and Kuldip K. Paliwal. "Modulation-domain Kalman filtering for single-channel speech enhancement." Speech Communication 53, no. 6 (July 2011): 818–29. http://dx.doi.org/10.1016/j.specom.2011.02.001.

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44

Moore, Robert E., Elizabeth M. Adams, Paul A. Dagenais, and Carrie Caffee. "Effects of reverberation and filtering on speech rate judgment." International Journal of Audiology 46, no. 3 (January 2007): 154–60. http://dx.doi.org/10.1080/14992020601126831.

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45

Al-Haddad. "Robust Speech Recognition Using Fusion Techniques and Adaptive Filtering." American Journal of Applied Sciences 6, no. 2 (February 1, 2009): 290–95. http://dx.doi.org/10.3844/ajassp.2009.290.295.

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46

Veeneman, D., and S. BeMent. "Automatic glottal inverse filtering from speech and electroglottographic signals." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 2 (April 1985): 369–77. http://dx.doi.org/10.1109/tassp.1985.1164544.

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47

Ban, Sung-Min, and Hyung-Soon Kim. "ARMA Filtering of Speech Features Using Energy Based Weights." Journal of the Acoustical Society of Korea 31, no. 2 (February 29, 2012): 87–92. http://dx.doi.org/10.7776/ask.2012.31.2.087.

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48

Fujioka, Kazuma, Noboru Hayasaka, Yoshikazu Miyanaga, and Norinobu Yoshida. "Noise reduction of speech signals by running spectrum filtering." Systems and Computers in Japan 37, no. 14 (2006): 52–61. http://dx.doi.org/10.1002/scj.20529.

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49

Al-Haddad, S. A. R., S. A. Samad, A. Hussain, K. A. Ishak, and A. O. A. Noor. "Robust Speech Recognition Using Fusion Techniques and Adaptive Filtering." American Journal of Applied Sciences 6, no. 2 (February 1, 2009): 290–95. http://dx.doi.org/10.3844/ajas.2009.290.295.

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50

Sun, Chengli, and Junsheng Mu. "An eigenvalue filtering based subspace approach for speech enhancement." Noise Control Engineering Journal 63, no. 1 (January 1, 2015): 36–48. http://dx.doi.org/10.3397/1/376305.

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