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Relevant bibliographies by topics / Speech enhancement algorithm

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Academic literature on the topic 'Speech enhancement algorithm'

Author: Grafiati

Published: 19 February 2023

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Journal articles on the topic "Speech enhancement algorithm"

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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 (September 2, 2020): 6077. http://dx.doi.org/10.3390/app10176077.

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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 classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

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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.

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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 distortion ratio. The distortion in excess of 6.02dB can be properly controlled via tuning the gain function of the speech enhancement algorithm. The experiment results show that the proposed algorithm can improve the intelligibility of the noisy speech considerably.

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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.

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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 in excess of 6.02 dB which damages intelligibility much. Both objective evaluation and subjective audition show that the proposed algorithm does improve the enhanced speech intelligibility.

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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.

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Gnanamanickam, Jenifa, Yuvaraj Natarajan, and Sri Preethaa K. R. "A Hybrid Speech Enhancement Algorithm for Voice Assistance Application." Sensors 21, no. 21 (October 23, 2021): 7025. http://dx.doi.org/10.3390/s21217025.

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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 communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

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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.

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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 reduction of the speech distortion and residual musical noise.

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Shanmukha, J., K. S.Ramesh, and S. Koteswar Rao. "Speech enhancement using discrete kalmanfilter algorithm." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 249. http://dx.doi.org/10.14419/ijet.v7i2.7.10590.

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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.

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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.

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

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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 microphone matrix is proposed in this paper. It consists of three parts. The first part is the preprocessing, divided into two branches: the upper branch enhances the speech signal, and the lower branch gets the noise. The second part is the optimized improved minimum controlled recursive averaging. The noise power spectrum is updated not only in the non-speech segments but also in the speech segments. Fi- nally, according to the estimated noise power spectrum, the minimum mean-square error log-spectral amplitude algorithm is used to enhance speech. Testing data are from TIMIT and Noisex-92 databases. Short-time objective intelligibility and seg- mental signal-to-noise ratio are chosen as evaluation metrics. Experimental results show that the proposed speech enhancement algorithm can improve the segmental signal-to-noise ratio and short-time objective intelligibility for various noise types at different signal-to-noise ratio levels.

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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.

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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 background noise. Based on the test results of the algorithm on PC platform, the system meets the design requirements and the effect is good.

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Dissertations / Theses on the topic "Speech enhancement algorithm"

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

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

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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)-based speech enhancement, each clean speech frame is represented by an auto-regressive (AR) process, whose parameters comprise the linear prediction coefficients (LPCs) and prediction error variance. The LPC parameters and the additive noise variance are used to form the recursive equations of the KF. In augmented KF (AKF), both the clean speech and additive noise LPC parameters are incorporated into an augmented matrix to construct the recursive equations of AKF. Given a frame of noisy speech samples, the KF and AKF give a linear MMSE estimate of the clean speech samples using the recursive equations. Usually, the inaccurate estimates of the parameters introduce bias in the KF and AKF gain, leading to a degradation in speech enhancement performance. The research contributions in this dissertation can be grouped into three focus areas. In the first work, we propose an iterative KF (IT-KF) to offset the bias in KF gain for speech enhancement through utilizing the parameters in real-life noise conditions. In the second work, we jointly incorporate the robustness and sensitivity metrics to offset the bias in the KF and AKF gain - which address speech enhancement in real-life noise conditions. The third focus area consists of the deep neural network (DNN) and whitening filter assisted KF and AKF for speech enhancement. Specifically, DNN and whitening filter-based approaches utilize the parameter estimates for the KF and AKF for speech enhancement. However, the whitening filter still produces biased speech LPC estimates for the KF and AKF, results in degraded speech. To address this, we propose a DeepLPC framework constructed with the state-of-the-art residual network and temporal convolutional network (ResNet-TCN) to jointly estimate the speech and noise LPC parameters from the noisy speech for the AKF. Recently, the multi-head self-attention network (MHANet) has demonstrated the ability to more efficiently model the long-term dependencies of noisy speech than ResNet-TCN. Therefore, we employ the MHANet within DeepLPC, termed as DeepLPC-MHANet, to further improve the speech and noise LPC parameter estimates for the AKF. Finally, we perform a comprehensive study on four different training targets for LPC estimation using ResNet-TCN and MHANet. This study aims to determine which training target as well as DNN method produces accurate speech and noise LPC parameter with an application of AKF-based speech enhancement in practice. Objective and subjective scores demonstrate that the proposed methods in this dissertation produce enhanced speech with higher quality and intelligibility than the competing methods in various noise conditions for a wide range of signal-to-noise ratio (SNR) levels.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
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Shannon, Benjamin J. "Speech Recognition and Enhancement using Autocorrelation Domain Processing." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365193.

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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. Contributing to the overall deficiency of the system is the performance of current feature extraction methods. These techniques cannot be described as robust when deployed in the dynamic acoustic environments typically encountered in everyday life. Ambient background noise also affects speech communication between humans. Restoration of a degraded speech signal by a speech enhancement algorithm can help to reduce this effect. Techniques developed for improving the noise robustness of feature extraction algorithms can also find application in speech enhancement algorithms. Contributions made in this thesis are aimed at improving the performance of automatic speech recognition in the presence of ambient acoustic noise and the quality of speech perceived by human listeners in the same conditions. The proposed techniques are based on processing the degraded speech signal in the ii autocorrelation domain. Based on the differences in the production mechanisms of speech and noise signals, transforming them into the autocorrelation domain provides a favourable representation for noise robust processing. We found that by utilising the higher-lag coefficients of the autocorrelation sequence and discarding the lower-lag coefficients, more noise robust spectral estimates could be made. This approach was found to be adept at suppressing particular classes of non-stationary noise that conventional methods fail to handle. We also explored a topic in speech enhancement of phase spectrum estimation and showed positive results. The proposed feature extraction and speech enhancement techniques, while performing very well for some non-stationary noises, were less effective against the stationary cases. This work highlights the autocorrelation domain as a domain for noise robust speech processing in the presence of dynamic ambient noises. With improvements in short-time autocorrelation estimation, it is expected that the performance of the techniques for stationary noises can also be improved.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
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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.

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

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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. The algorithms are derived from the Bayesian theory of estimation and can be grouped according to i) the STFT representation they estimate ii) the estimator they apply and iii) the speech prior density they assume. Apart from the introduction of algorithms that surpass the performance of similar algorithms that exist in the literature, the compilation of the above framework offers insight on the effect and relative importance of the different components of the algorithms (e.g. prior, estimator) to the quality of the enhanced speech. In the second part of this thesis, we develop methods for the estimation of the power of time varying noise. The main outcome is a method that exploits some similarities between the distribution of the noisy speech spectral amplitude coefficients within a single frequency bin, and the corresponding distribution of the corrupting noise. The above similarities allow the extraction of samples that are more likely to correspond to noise, from a window of past spectral amplitude observations. The extracted samples are then used to produce an estimate of the noise power. In the final part of this thesis, we are concerned with the incorporation of the time and frequency dependencies of speech signals in our estimation model. The theoretical framework on which the modelling is based is provided by Markov Random Fields (MRF’s). Initially, we develop a MAP estimator of speech based on the Gaussian MRF prior. In the following, we introduce the Chi MRF, which is employed in the development of an improved speech estimator. Finally, the performance of fixed and adaptive schemes for the estimation of the MRF parameters is investigated.

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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.

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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.

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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 feasibility of combining masking properties with a Kalman filter. Some heuristic methods are also proposed for an easier implementation. One algorithm proposes to use the frequency domain masking level as a hard threshold to reshape the Kalman filtered signal. Another algorithm is to use a post-filter concatenated with the Kalman filter, using a threshold where both time-domain and frequency domain masking properties are taken into account. The goal of the masking is to make the energy of the estimate state error smaller than the threshold. To further decrease the computational cost, a wavelet Kalman filter combined with masking thresholds is also introduced. In the above algorithms, the speech model is assumed to be linear. Nonlinear speech models are also considered in the thesis. To address the nonlinear model problem, dual Extended Kalman Filter (EKF) and dual Unscented Kalman Filter (UKF) algorithms are studied. In these cases, both time-domain and frequency domain masking properties are taken into account. The simulation results show that all the proposed methods combining Kalman filter and masking properties can produce promising results from the point of view of PESQ scores. The average PESQ score gains obtained by these proposed methods are from about 0.35 to 0.45. Some informal subjective tests also show that the performance of the proposed methods is promising. No voice activity detection is required in the proposed methods.

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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.

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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.

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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 studies were carried out for specifying the significant parameters and values for using in MRT. For the HLS study, twelve hearing impaired subjects listened to unprocessed sounds and thirty six normal hearing subjects listened to simulated sounds. In the evaluation of the HLS, both measures gave similar and consistent results for both unprocessed and simulated sounds. In FLMs study, hearing impaired subjects were simulated and normal hearing subjects listened to frequency lowered sounds with the specified methods, parameters and values. All FLMs were compared with the standard method of hearing aids (amplification) for five different noisy environments. FLMs satisfied 83% success of higher speech intelligibility improvement than amplification in all cases. As a conclusion, the necessity of using subject-specific FLMs was shown to achieve higher intelligibility than with amplification only. Accordingly, a methodology for selection of the values of parameters for different noisy environments and for different audiograms was developed.

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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.

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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.

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Books on the topic "Speech enhancement algorithm"

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

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

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

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Book chapters on the topic "Speech enhancement algorithm"

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Based on Bat Algorithm (BA)." In Metaheuristic Applications to Speech Enhancement, 91–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_8.

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)." In Metaheuristic Applications to Speech Enhancement, 61–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_6.

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Djendi, Mohamed, Feriel Khemies, and Amina Morsli. "A Frequency Domain Adaptive Decorrelating Algorithm for Speech Enhancement." In Speech and Computer, 51–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23132-7_6.

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Pei, Dong. "Research on Speech Enhancement Algorithm in Intelligent Speech System." In 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning, 337–45. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1_39.

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Dai, Xuzheng, Baoxian Yu, and Xianhua Dai. "An Improved Signal Subspace Algorithm for Speech Enhancement." In IFIP Advances in Information and Communication Technology, 104–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45526-5_10.

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Liu, Lilan, Gan Sun, Zenggui Gao, and Yi Wang. "Analysis of Speech Enhancement Algorithm in Industrial Noise Environment." In Advanced Manufacturing and Automation VIII, 226–36. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2375-1_29.

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Li, Jin, Fu Liu, Huiyan Xu, and Feile Wang. "Speech Enhancement Algorithm Based on Hilbert-Huang and Wavelet." In Lecture Notes in Electrical Engineering, 173–78. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4790-9_23.

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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, 221–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0443-0_18.

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Apte, S. D., and Shridhar. "An Efficient Speech Enhancement Algorithm Using Conjugate Symmetry of DFT." In Electrical Engineering and Control, 695–701. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21765-4_87.

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Djendi, Mohamed, and Meriem Zoulikha. "A New Robust Blind Source Separation Algorithm for Speech Enhancement." In Advanced Control Engineering Methods in Electrical Engineering Systems, 526–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97816-1_40.

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Conference papers on the topic "Speech enhancement algorithm"

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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.

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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.

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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.

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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.

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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.

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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.

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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: ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-532.

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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.

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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.

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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.

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