Log in

Relevant bibliographies by topics / Speech enhancement algorithm

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers

Academic literature on the topic 'Speech enhancement algorithm'

Author: Grafiati

Published: 19 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Speech enhancement algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Speech enhancement algorithm"

1

Park, Gyuseok, Woohyeong Cho, Kyu-Sung Kim, and Sangmin Lee. "Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises." Applied Sciences 10, no. 17 (2020): 6077. http://dx.doi.org/10.3390/app10176077.

Full text

Abstract:

Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classificati

APA, Harvard, Vancouver, ISO, and other styles

2

Yang, Yu Xiang, and Jian Fen Ma. "Speech Intelligibility Enhancement Using Distortion Control." Advanced Materials Research 912-914 (April 2014): 1391–94. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1391.

Full text

Abstract:

In order to improve the intelligibility of the noisy speech, a novel speech enhancement algorithm using distortion control is proposed. The reason why current speech enhancement algorithm cannot improve speech intelligibility is that these algorithms aim to minimize the overall distortion of the enhanced speech. However, different speech distortions make different contributions to the speech intelligibility. The distortion in excess of 6.02dB has the most detrimental effects on speech intelligibility. In the process of noise reduction, the type of speech distortion can be determined by signal

APA, Harvard, Vancouver, ISO, and other styles

3

Liu, Peng, and Jian Fen Ma. "A Higher Intelligibility Speech-Enhancement Algorithm." Applied Mechanics and Materials 321-324 (June 2013): 1075–79. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1075.

Full text

Abstract:

A higher intelligibility speech-enhancement algorithm based on subspace is proposed. The majority existing speech-enhancement algorithms cannot effectively improve enhanced speech intelligibility. One important reason is that they only use Minimum Mean Square Error (MMSE) to constrain speech distortion but ignore that speech distortion region differences have a significant effect on intelligibility. A priori Signal Noise Ratio (SNR) and gain matrix were used to determine the distortion region. Then the gain matrix was modified to constrain the magnitude spectrum of the amplification distortion

APA, Harvard, Vancouver, ISO, and other styles

4

韩, 蕊蕊. "Speech Enhancement Algorithm Combining Speech Absence Probability." Hans Journal of Wireless Communications 08, no. 04 (2018): 141–47. http://dx.doi.org/10.12677/hjwc.2018.84016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Gnanamanickam, Jenifa, Yuvaraj Natarajan, and Sri Preethaa K. R. "A Hybrid Speech Enhancement Algorithm for Voice Assistance Application." Sensors 21, no. 21 (2021): 7025. http://dx.doi.org/10.3390/s21217025.

Full text

Abstract:

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When

APA, Harvard, Vancouver, ISO, and other styles

6

Liu, Yu Hong, Dong Mei Zhou, and Zhan Jun Jiang. "Improved Spectral Subtraction Speech Enhancement Algorithm." Advanced Materials Research 760-762 (September 2013): 536–41. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.536.

Full text

Abstract:

The paper addresses the problems of speech distortion and residual musical noise introduced by conventional spectral subtraction (SS) method for speech enhancement. In this paper, we propose a modified SS algorithm for speech enhancement based on the masking properties of human auditory system. The algorithm computes the parameters α and β dynamically according to the masking thresholds of the critical frequency segments for each speech frame. Simulation results show that the proposed algorithm is superior to the conventional SS method, not only in the improvement of output SNR, but in the red

APA, Harvard, Vancouver, ISO, and other styles

7

Shanmukha, J., K. S.Ramesh, and S. Koteswar Rao. "Speech enhancement using discrete kalmanfilter algorithm." International Journal of Engineering & Technology 7, no. 2.7 (2018): 249. http://dx.doi.org/10.14419/ijet.v7i2.7.10590.

Full text

Abstract:

Enhancement of speech deals with the noise reduction which is mostly essential in many applications like teleconferencing sys tem. The nearness of added substance foundation noise degenerate the discourse in correspondence condition. In the event that the commotion is developing slower than discourse which implies noise is more stationary than speech .Estimation of noise ends up noticeably s impler when delays happened in speech. In this work discrete Kalman channel calculation which gauges the inward condition of a direct powe r-ful framework.

APA, Harvard, Vancouver, ISO, and other styles

8

Yuan, Zhong-Xuan, Soo Ngee Koh, and Ing Yann Soon. "Speech enhancement based on hybrid algorithm." Electronics Letters 35, no. 20 (1999): 1710. http://dx.doi.org/10.1049/el:19991162.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Li, Qiuying, Tao Zhang, Yanzhang Geng, and Zhen Gao. "Microphone array speech enhancement based on optimized IMCRA." Noise Control Engineering Journal 69, no. 6 (2021): 468–76. http://dx.doi.org/10.3397/1/376944.

Full text

Abstract:

Microphone array speech enhancement algorithm uses temporal and spatial informa- tion to improve the performance of speech noise reduction significantly. By combining noise estimation algorithm with microphone array speech enhancement, the accuracy of noise estimation is improved, and the computation is reduced. In traditional noise es- timation algorithms, the noise power spectrum is not updated in the presence of speech, which leads to the delay and deviation of noise spectrum estimation. An optimized im- proved minimum controlled recursion average speech enhancement algorithm, based on a mi

APA, Harvard, Vancouver, ISO, and other styles

10

Liu, Hongmei, and Yan Li. "Speech enhancement system design is based on FastICA blind source separation algorithm." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 57–62. http://dx.doi.org/10.54097/hset.v7i.997.

Full text

Abstract:

Based on the analysis of existing speech enhancement based on the drawbacks of the traditional algorithm is adopted in the system is proposed based on FastICA blind source separation algorithm design of speech enhancement algorithm, and the transplanted to embedded speech enhancement system. The system real-time speech enhancement, by four element microphone arrays is used to sample the space of sound signal and through the built-in speech enhancement algorithm to the voice source signal and noise source signal separation, capable of suppressing co channel noise, active noise and residual back

APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Speech enhancement algorithm"

1

Gagnon, Luc. "A speech enhancement algorithm based upon resonator filterbanks." Thesis, University of Ottawa (Canada), 1991. http://hdl.handle.net/10393/7767.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Roy, Sujan K. "Kalman Filtering with Machine Learning Methods for Speech Enhancement." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/404456.

Full text

Abstract:

Speech corrupted by background noise (or noisy speech) can reduce the efficiency of communication between man-man and man-machine. A speech enhancement algorithm (SEA) can be used to suppress the embedded background noise and increase the quality and intelligibility of noisy speech. Many applications, such as speech communication systems, hearing aid devices, and speech recognition systems, typically rely upon speech enhancement algorithms for robustness. This dissertation focuses on single-channel speech enhancement using Kalman filtering with machine learning methods. In Kalman filter (KF)-b

APA, Harvard, Vancouver, ISO, and other styles

3

Shannon, Benjamin J. "Speech Recognition and Enhancement using Autocorrelation Domain Processing." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365193.

Full text

Abstract:

From a young age, humans learn language skills and develop them to the point that they become reflex like. As a communication modality, speech is efficient, natural and intrinsically understood. By developing spoken language interfaces for machines, the same kinds of benefits can be realised for the human-machine interaction. Development of machine based speech recognition has been in progress for the past 50 years. In this time significant advances have been made, but the performance of current solutions in the presence of ambient acoustic noise is one factor holding the technology back. Cont

APA, Harvard, Vancouver, ISO, and other styles

4

Freesen, Jessica Stacy. "Evaluation of the telephone speech enhancement algorithm in older adults using individual audiograms." Connect to resource, 2009. http://hdl.handle.net/1811/37214.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Andrianakis, Ioannis. "Bayesian algorithms for speech enhancement." Thesis, University of Southampton, 2007. https://eprints.soton.ac.uk/66244/.

Full text

Abstract:

The portability of modern voice processing devices allows them to be used in environments where background noise conditions can be adverse. Background noise can deteriorate the quality of speech transmitted through such devices, but speech enhancement algorithms can ameliorate this degradation to some extent. The development of speech enhancement algorithms that improve the quality of noisy speech is the aim of this thesis, which consists of three main parts. In the first part, we propose a framework of algorithms that estimate the clean speech Short Time Fourier Transform (STFT) coefficients.

APA, Harvard, Vancouver, ISO, and other styles

6

O'Rourke, William Thomas. "Real-world evaluation of mobile phone speech enhancement algorithms." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000585.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Ma, Ning. "Speech enhancement algorithms using Kalman filtering and masking properties of human auditory systems." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/29229.

Full text

Abstract:

Speech enhancement algorithms have been employed successfully in many areas such as VoIP, automatic speech recognition and speaker verification. Many approaches are presented in the literature. This thesis focuses on enhancing single channel speech degraded by white noise or colored noise. A Kalman filter algorithm combined with the masking properties of human auditory systems is proposed. The threshold computed from the masking properties is used as a constraint in the Kalman filter to theoretically derive a modified Kalman filter. The derivation gives a theoretical foundation for the feasibi

APA, Harvard, Vancouver, ISO, and other styles

8

Sabuwala, Adnan H. "Towards a real-time implementation of loudness enhancement algorithms on a Motorola DSP 56600." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000602.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Arioz, Umut. "Developing Subject-specific Frequency Lowering Algorithms With Simulated Hearing Loss For The Enhancement Of Sensorineural Hearing Loss." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614929/index.pdf.

Full text

Abstract:

The hearing and understanding problems of the people with high frequency hearing loss are covered within the scope of this thesis. For overcoming these problems, two main studies, developing hearing loss simulation (HLS) and applying new frequency lowering methods (FLMs) were carried out. HLS was developed with the suprathreshold effects and new FLMs were applied with different combinations of the FLMs. For evaluating the studies, modified rhyme test (MRT) and speech intelligibility index (SII) were used as subjective and objective measures, respectively. Before both of the studies, offline st

APA, Harvard, Vancouver, ISO, and other styles

10

Al-Ali, Ahmed Kamil Hasan. "Forensic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130783/1/Ahmed%20Kamil%20Hasan_Al-Ali_Thesis.pdf.

Full text

Abstract:

The performance of forensic speaker recognition systems degrades significantly in the presence of environmental noise and reverberant conditions. This research developed new techniques to improve forensic speaker recognition performance under these conditions using fusion feature extraction techniques and speech enhancement based on the independent component analysis algorithm. A range of forensic speaker recognition applications will benefit from the research outcomes including criminal investigations and law enforcement agencies.

APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Speech enhancement algorithm"

1

Kunche, Prajna, and K. V. V. S. Reddy. Metaheuristic Applications to Speech Enhancement. Springer London, Limited, 2016.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Kunche, Prajna, and K.V.V.S. Reddy. Metaheuristic Applications to Speech Enhancement. Springer, 2016.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ramakrishnan, S., ed. Speech Enhancement, Modeling and Recognition- Algorithms and Applications. InTech, 2012. http://dx.doi.org/10.5772/2391.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Speech enhancement algorithm"

1

Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Based on Bat Algorithm (BA)." In Metaheuristic Applications to Speech Enhancement. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)." In Metaheuristic Applications to Speech Enhancement. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Djendi, Mohamed, Feriel Khemies, and Amina Morsli. "A Frequency Domain Adaptive Decorrelating Algorithm for Speech Enhancement." In Speech and Computer. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23132-7_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Pei, Dong. "Research on Speech Enhancement Algorithm in Intelligent Speech System." In 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1_39.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Dai, Xuzheng, Baoxian Yu, and Xianhua Dai. "An Improved Signal Subspace Algorithm for Speech Enhancement." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45526-5_10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Liu, Lilan, Gan Sun, Zenggui Gao, and Yi Wang. "Analysis of Speech Enhancement Algorithm in Industrial Noise Environment." In Advanced Manufacturing and Automation VIII. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2375-1_29.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Li, Jin, Fu Liu, Huiyan Xu, and Feile Wang. "Speech Enhancement Algorithm Based on Hilbert-Huang and Wavelet." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4790-9_23.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Kumar, Raj, Manoj Tripathy, and R. S. Anand. "Iterative Thresholding-Based Spectral Subtraction Algorithm for Speech Enhancement." In Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0443-0_18.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Apte, S. D., and Shridhar. "An Efficient Speech Enhancement Algorithm Using Conjugate Symmetry of DFT." In Electrical Engineering and Control. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21765-4_87.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Djendi, Mohamed, and Meriem Zoulikha. "A New Robust Blind Source Separation Algorithm for Speech Enhancement." In Advanced Control Engineering Methods in Electrical Engineering Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97816-1_40.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Speech enhancement algorithm"

1

Schwartz, Boaz, Sharon Gannot, and Emanuel A. P. Habets. "LPC-based speech dereverberation using Kalman-EM algorithm." In 2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2014. http://dx.doi.org/10.1109/iwaenc.2014.6953329.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Krishnamoorthi, Harish, Andreas Spanias, Visar Berisha, Homin Kwon, and Harvey Thornburg. "An auditory-domain based speech enhancement algorithm." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495147.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Qian, Long, Fujian Zheng, Xin Guo, Yingxiang Zuo, and Wei Zhou. "Vehicle Speech Enhancement Algorithm Based on TanhDBN." In 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). IEEE, 2020. http://dx.doi.org/10.1109/iicspi51290.2020.9332320.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Yang, Hongwu, Dongliang Hao, Hongyin Sun, and Yitong Liu. "Speech enhancement using orthogonal matching pursuit algorithm." In 2014 IEEE International Conference on Orange Technologies (ICOT). IEEE, 2014. http://dx.doi.org/10.1109/icot.2014.6956609.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ma, Yongbao, Yi Zhou, Jingang Liu, Jie Xia, and Hongqing Liu. "An improved switch speech enhancement algorithm for automatic speech recognition." In 2015 IEEE International Conference on Computer and Communications (ICCC). IEEE, 2015. http://dx.doi.org/10.1109/compcomm.2015.7387610.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Zhao, Gaihua, Bin Zhou, Xiongwei Zhang, and Sui Lu-ying. "A new speech enhancement algorithm with generalized Gamma speech model." In 2012 International Conference on Wireless Communications & Signal Processing (WCSP 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcsp.2012.6542803.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Fu, Guokang, and Ta-Hsin Li. "A segment-based algorithm of speech enhancement for robust speech recognition." In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-532.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Li, Siqi, Shouhao Wu, Yongjie Wang, Wenxiu Guo, and Youling Zhou. "An improved NLMS algorithm based on speech enhancement." In 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2015. http://dx.doi.org/10.1109/iaeac.2015.7428686.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Zou, Xin, Peter Jancovic, Ju Liu, and Mulnevver Kokuer. "ICA-Based MAP Algorithm for Speech Signal Enhancement." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366976.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Mahabadi, Ali Ameri, and Mohammad Eshghi. "Speech enhancement using Affine Projection Algorithm in subband." In 2009 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2009. http://dx.doi.org/10.1109/mmcs.2009.5256698.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!