Journal articles: '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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Ch.D, Umasankar. "Design and Implementation of Normalized Hybrid Projection Algorithm for Speech Enhancement." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (February 13, 2020): 223–32. http://dx.doi.org/10.5373/jardcs/v12i1/20201033.

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S, Siva Priyanka, and Kishore Kumar T. "Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement." Traitement du Signal 38, no. 3 (June 30, 2021): 785–95. http://dx.doi.org/10.18280/ts.380325.

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In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

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Dachasilaruk, Siriporn, Niphat Jantharamin, and Apichai Rungruang. "Speech intelligibility enhancement for Thai-speaking cochlear implant listeners." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 866. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp866-875.

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Cochlear implant (CI) listeners encounter difficulties in communicating with other persons in noisy listening environments. However, most CI research has been carried out using the English language. In this study, single-channel speech enhancement (SE) strategies as a pre-processing approach for the CI system were investigated in terms of Thai speech intelligibility improvement. Two SE algorithms, namely multi-band spectral subtraction (MBSS) and Weiner filter (WF) algorithms, were evaluated. Speech signals consisting of monosyllabic and bisyllabic Thai words were degraded by speech-shaped noise and babble noise at SNR levels of 0, 5, and 10 dB. Then the noisy words were enhanced using SE algorithms. The enhanced words were fed into the CI system to synthesize vocoded speech. The vocoded speech was presented to twenty normal-hearing listeners. The results indicated that speech intelligibility was marginally improved by the MBSS algorithm and significantly improved by the WF algorithm in some conditions. The enhanced bisyllabic words showed a noticeably higher intelligibility improvement than the enhanced monosyllabic words in all conditions, particularly in speech-shaped noise. Such outcomes may be beneficial to Thai-speaking CI listeners.

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Wang, Dong-xia, Mao-song Jiang, Fang-lin Niu, Yu-dong Cao, and Cheng-xu Zhou. "Speech Enhancement Control Design Algorithm for Dual-Microphone Systems Using β-NMF in a Complex Environment." Complexity 2018 (September 9, 2018): 1–13. http://dx.doi.org/10.1155/2018/6153451.

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Single-microphone speech enhancement algorithms by using nonnegative matrix factorization can only utilize the temporal and spectral diversity of the received signal, making the performance of the noise suppression degrade rapidly in a complex environment. Microphone arrays have spatial selection and high signal gain, so it applies to the adverse noise conditions. In this paper, we present a new algorithm for speech enhancement based on two microphones with nonnegative matrix factorization. The interchannel characteristic of each nonnegative matrix factorization basis can be modeled by the adopted method, such as the amplitude ratios and the phase differences between channels. The results of the experiment confirm that the proposed algorithm is superior to other dual-microphone speech enhancement algorithms.

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Ghorpade, Kalpana, and Arti Khaparde. "SINGLE CHANNEL SPEECH ENHANCEMENT USING EVOLUTIONARY ALGORITHM WITH LOG-MMSE." ASEAN Engineering Journal 12, no. 1 (February 28, 2022): 83–91. http://dx.doi.org/10.11113/aej.v12.16770.

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Additive noise degrades speech quality and intelligibility. Speech enhancement reduces this noise to make speech more pleasant and intelligible. It plays a significant role in speech recognition or speech-operated systems. In this paper, we propose a single-channel speech enhancement method in which the log-minimum mean square error method (log-MMSE) and modified accelerated particle swarm optimization algorithm are used to design a filter for improving the quality and intelligibility of noisy speech. Accelerated particle swarm optimization (APSO) algorithm is modified in which a single dimension of particle position is changed in a single iteration while obtaining the particle’s new position. Using this algorithm, a filter is designed with multiple passbands and notches for speech enhancement. The modified algorithm converges faster compared with standard particle swarm optimization algorithm (PSO) and APSO giving optimum filter coefficients. The designed filter is used to enhance the speech. The proposed speech enhancement method improves the perceptual estimation of speech quality (PESQ) by 17.05% for 5dB babble noise, 33.92 % for 5dB car noise, 14.96 % for 5dB airport noise, and 39.13 % for 5dB exhibition noise. The average output PESQ for these four types of noise is improved compared to conventional methods of speech enhancement. There is an average of 7.58 dB improvement in segmental SNR for these noise types. The proposed method improves speech intelligibility with minimum speech distortion.

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LEE, Gihyoun, Sung Dae NA, KiWoong SEONG, Jin-Ho CHO, and Myoung Nam KIM. "Speech Enhancement Algorithm Using Recursive Wavelet Shrinkage." IEICE Transactions on Information and Systems E99.D, no. 7 (2016): 1945–48. http://dx.doi.org/10.1587/transinf.2015edl8251.

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Kalamani, M., S. Valarmathy, and M. Krishnamoorthi. "Modified noise reduction algorithm for speech enhancement." Applied Mathematical Sciences 8 (2014): 4447–52. http://dx.doi.org/10.12988/ams.2014.45365.

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Yanlei, Zhao, Ou Shifeng, and Gao Ying. "Improved Wiener Filter Algorithm for Speech Enhancement." Automation, Control and Intelligent Systems 7, no. 3 (2019): 92. http://dx.doi.org/10.11648/j.acis.20190703.13.

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Lin, L., W. H. Holmes, and E. Ambikairajah. "Adaptive noise estimation algorithm for speech enhancement." Electronics Letters 39, no. 9 (2003): 754. http://dx.doi.org/10.1049/el:20030480.

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Sulong, Amart, Teddy S. Gunawan, Othman O. Khalifa, and Jalel Chebil. "Speech Enhancement based on Compressive Sensing Algorithm." IOP Conference Series: Materials Science and Engineering 53 (December 20, 2013): 012076. http://dx.doi.org/10.1088/1757-899x/53/1/012076.

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Hao, Xueying, Dali Zhu, Xianlan Wang, Long Yang, and Hualin Zeng. "A Speech Enhancement Algorithm for Speech Reconstruction Based on Laser Speckle Images." Sensors 23, no. 1 (December 28, 2022): 330. http://dx.doi.org/10.3390/s23010330.

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In the optical system for reconstructing speech signals based on laser speckle images, the resonance between the sound source and nearby objects leads to frequency response problem, which seriously affects the accuracy of reconstructed speech. In this paper, we propose a speech enhancement algorithm to reduce the frequency response. The results show that after using the speech enhancement algorithm, the frequency spectrum correlation coefficient between the reconstructed sinusoidal signal and the original sinusoidal signal is improved by up to 82.45%, and the real speech signal is improved by up to 56.40%. This proves that the speech enhancement algorithm is a valuable tool for solving the frequency response problem and improving the accuracy of reconstructed speech.

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Li, Xinqiang, Xingmian Wang, Yanan Qin, and Jing Li. "SNR Classification Based Multi-Estimator IRM Speech Enhancement Algorithm." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012086. http://dx.doi.org/10.1088/1742-6596/2173/1/012086.

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Abstract Deep neural network(DNN)-based ideal ratio mask(IRM) estimation methods are often adopted in speech enhancement tasks. In the previous work, IRM estimation was usually realized by a single DNN-based IRM estimator without considering the SNR levels, which had a limited performance in real applications. Therefore, a two stage speech enhancement method is proposed in this paper. Firstly, a DNN-based SNR classifier is employed to classify the speech frames into three classes according to different SNR thresholds. Secondly, three corresponding DNN based IRM estimators related to the three SNR classes are trained respectively, from which the amplitude spectrum is corrected. Finally, speech enhancement is realized by doing IDFT to the corrected speech spectrum combined with the phase information of noisy speech. Experiment results show that the algorithm proposed in this paper has better performances in the evaluation of short time objective intelligibility(STOI), perceptual evaluation of speech quality(PESQ) and segmental signal-to-noise ratio improvement(SSNRI) scores.

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Sun, Pengfei, and Jun Qin. "Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors." Archives of Acoustics 41, no. 3 (September 1, 2016): 579–90. http://dx.doi.org/10.1515/aoa-2016-0056.

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Abstract Despite various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially in low signalto-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager-energyoperators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noises (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW).

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Nidhyananthan, S. Selva, R. Shantha Selva Kumari, and A. Arun Prakash. "A review on speech enhancement algorithms and why to combine with environment classification." International Journal of Modern Physics C 25, no. 10 (September 11, 2014): 1430002. http://dx.doi.org/10.1142/s0129183114300024.

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Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is still the most challenging problem, because most papers rely on estimating the noise during the nonspeech activity assuming that the background noise is uncorrelated (statistically independent of speech signal), nonstationary and slowly varying, so that the noise characteristics estimated in the absence of speech can be used subsequently in the presence of speech, whereas in a real time environment such assumptions do not hold for all the time. In this paper, we discuss the historical development of approaches that starts from the year 1970 to, the recent, 2013 for enhancing the noisy speech corrupted by additive background noise. Seeing the history, there are algorithms that enhance the noisy speech very well as long as a specific application is concerned such as the In-car noisy environments. It has to be observed that a speech enhancement algorithm performs well with a good estimation of the noise Power Spectral Density (PSD) from the noisy speech. Our idea pops up based on this observation, for online speech enhancement (i.e. in a real time environment) such as mobile phone applications, instead of estimating the noise from the noisy speech alone, the system should be able to monitor an environment continuously and classify it. Based on the current environment of the user, the system should adapt the algorithm (i.e. enhancement or estimation algorithm) for the current environment to enhance the noisy speech.

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Wu, Cai Yun. "Speech Enhancement System Based on Lyapunov FXLMS Algorithm." Advanced Materials Research 490-495 (March 2012): 1670–74. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1670.

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This paper studies the speech enhancement technology. A Lyapunov function of the tracking error systems is defined, and the adaptive filter based on FXLMS algorithm can make the error converge to zero asymptotically. It is shown to be suitable to solve the problem of the speech enhancement from computer simulations

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Jyoshna, Girika, and Md Zia Ur Rahman. "An Intelligent reference free adaptive learning algorithm for speech enhancement." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1895–906. http://dx.doi.org/10.3233/jifs-211249.

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Removing of noise component is an important task in all practical applications like hearing aids, speech therapy etc. In speech communication applications the speech components are contaminated with various types of noises. Separation of speech and noise component is a key issue in hearing aids, speech therapy applications. This paper demonstrates a hybrid version of singular spectrum analysis (SSA) and independent component analysis (ICA) based adaptive noise canceller (ANC) to separate noise and speech components. As ICA is not suitable for single channel sources, SSA is used to map signal channel data to multivariant data. Therefore, SSA based ICA decomposition is used to generate reference for noise cancellation process. Variable Step based adaptive learning algorithm is used to separate noise contaminations from speech signals. To reduce computational complexity of system, sign regressor operation is applied to the data vector of the proposed adaptive learning methodology. Performance measures such as Signal to noise ratio improvement, excess mean square error and misadjustment are calculated for various considered ANCs, their values for crane noise are 29.6633 dB, – 27.4854 dB and 0.2058 respectively. Among the various adaptive learning algorithms, sign regressor based step variable method performs better than the other algorithms. Hence this learning methodology is well suited for hearing aids and speech therapy applications due to its robustness, less computational complexity and filtering ability.

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Shaheen, Dima, Oumayma Al Dakkak, and Mohiedin Wainakh. "Incoherent Discriminative Dictionary Learning for Speech Enhancement." Journal of Telecommunications and Information Technology 3 (September 28, 2018): 42–54. http://dx.doi.org/10.26636/jtit.2018.121317.

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Speech enhancement is one of the many challenging tasks in signal processing, especially in the case of nonstationary speech-like noise. In this paper a new incoherent discriminative dictionary learning algorithm is proposed to model both speech and noise, where the cost function accounts for both “source confusion” and “source distortion” errors, with a regularization term that penalizes the coherence between speech and noise sub-dictionaries. At the enhancement stage, we use sparse coding on the learnt dictionary to ﬁnd an estimate for both clean speech and noise amplitude spectrum. In the ﬁnal phase, the Wiener ﬁlter is used to reﬁne the clean speech estimate. Experiments on the Noizeus dataset, using two objective speech enhancement measures: frequency-weighted segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) demonstrate that the proposed algorithm outperforms other speech enhancement methods tested.

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Dehghan Firoozabadi, Ali, Pablo Irarrazaval, Pablo Adasme, David Zabala-Blanco, Hugo Durney, Miguel Sanhueza, Pablo Palacios-Játiva, and Cesar Azurdia-Meza. "Multiresolution Speech Enhancement Based on Proposed Circular Nested Microphone Array in Combination with Sub-Band Affine Projection Algorithm." Applied Sciences 10, no. 11 (June 6, 2020): 3955. http://dx.doi.org/10.3390/app10113955.

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Speech enhancement is one of the most important fields in audio and speech signal processing. The speech enhancement methods are divided into the single and multi-channel algorithms. The multi-channel methods increase the speech enhancement performance by providing more information with the use of more microphones. In addition, spatial aliasing is one of the destructive factors in speech enhancement strategies. In this article, we first propose a uniform circular nested microphone array (CNMA) for data recording. The microphone array increases the accuracy of the speech processing methods by increasing the information. Moreover, the proposed nested structure eliminates the spatial aliasing between microphone signals. The circular shape in the proposed nested microphone array implements the speech enhancement algorithm with the same probability for the speakers in all directions. In addition, the speech signal information is different in frequency bands, where the sub-band processing is proposed by the use of the analysis filter bank. The frequency resolution is increased in low frequency components by implementing the proposed filter bank. Then, the affine projection algorithm (APA) is implemented as an adaptive filter on sub-bands that were obtained by the proposed nested microphone array and analysis filter bank. This algorithm adaptively enhances the noisy speech signal. Next, the synthesis filters are implemented for reconstructing the enhanced speech signal. The proposed circular nested microphone array in combination with the sub-band affine projection algorithm (CNMA-SBAPA) is compared with the least mean square (LMS), recursive least square (RLS), traditional APA, distributed multichannel Wiener filter (DB-MWF), and multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR) in terms of the segmental signal-to-noise ratio (SegSNR), perceptual evaluation of speech quality (PESQ), mean opinion score (MOS), short-time objective intelligibility (STOI), and speed of convergence on real and simulated data for white and colored noises. In all scenarios, the proposed method has high accuracy at different levels and noise types by the lower distortion in comparison with other works and, furthermore, the speed of convergence is higher than the compared researches.

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Yang, Yang. "A New Speech Enhancement Method Based on Genetic Algorithm." Advanced Materials Research 268-270 (July 2011): 969–74. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.969.

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In this paper a new algorithm for speech enhancement is presented. In the proposed approach, the effect of noise is reduced from the singular values as well as the singular vectors. We utilize the Genetic Algorithm for optimally setting the parameters needed for our proposed speech enhancement process. In the case that the additive noise does not have the white noise characteristics, the GSVD operation is used for subspace division. The results indicate the better performance of our proposed method in comparison with other well-known speech enhancement techniques.

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Upadhyay, Navneet, and Abhijit Karmakar. "A Multi-Band Speech Enhancement Algorithm Exploiting Iterative Processing for Enhancement of Single Channel Speech." Journal of Signal and Information Processing 04, no. 02 (2013): 197–211. http://dx.doi.org/10.4236/jsip.2013.42027.

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Jiao, Mingke, Lin Lou, Jie Hu, Xiliang Geng, Wenyuan Zhang, Peng Zhang, and Jianqi Wang. "A new speech enhancement algorithm for millimeter-wave radar speech sensor." Microwave and Optical Technology Letters 56, no. 5 (March 11, 2014): 1184–89. http://dx.doi.org/10.1002/mop.28294.

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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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Xue, Hui Jun, Sheng Li, Teng Jiao, Yang Zhang, Hao Lv, Xiao Yu, Guo Hua Lu, Hua Zhang, and Jian Qi Wang. "An Non-Contact Speech Enhancement Algorithm Based on Lifting Scheme." Applied Mechanics and Materials 513-517 (February 2014): 3813–17. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3813.

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The traditional method for detecting speech signal needs the help of microphone, which must be placed closely to the body of human beings. To some extent, this method would bring several inconveniences. Non-contact speech detection method breaks through the limitation of the traditional method, this new kind of speech obtaining method can detect speech signal quite well even in strong noisy background. However, this non-contact speech detecting system also produces some electromagnetic noise and circuit noise, which reduced the quality of radar speech signal. Therefore, based on the good time-frequency analyze performance, the lifting scheme was also proposed in this paper to remove noise from radar speech. Comparing to classical enhancement algorithm, such as spectral subtraction and Wiener filter, the proposed algorithm can remove the component of noise availably and reserve the original pure speech signal in a promising way.

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Yu, Bowen, and Qingning Zeng. "A Single-Channel Speech Enhancement Algorithm Combined with Time-Frequency Mask." Journal of Physics: Conference Series 2417, no. 1 (December 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2417/1/012021.

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Aiming at the problem of noise overestimation when the classical single-channel speech enhancement method suppresses noise, single-channel speech enhancement method on the basis of the time-frequency mask is put forward to improve the quality of speech enhancement and separation. First, we estimate the noise and prior signal-to-noise ratio from the noisy speech, calculate the time-frequency mask and finally combine noisy speech synthesis to enhance the speech. Adaptive time-frequency mask combines IBM and IRM to avoid over-suppressing noise. In this paper, noisy speech is processed by an improved time-frequency mask combined with SNR. The algorithm combines the advantages of the single-channel algorithm and time-frequency mask. The experimental results indicate that this method significantly improves the signal-to-noise ratio, is robust, and is easy to use.

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Mahmmod, Basheera M., Sadiq H. Abdulhussain, Marwah A. Naser, Muntadher Alsabah, and Jamila Mustafina. "Speech Enhancement Algorithm Based on a Hybrid Estimator." IOP Conference Series: Materials Science and Engineering 1090, no. 1 (March 1, 2021): 012102. http://dx.doi.org/10.1088/1757-899x/1090/1/012102.

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Hoang, Poul, Zheng-Hua Tan, Jan Mark De Haan, and Jesper Jensen. "The Minimum Overlap-Gap Algorithm for Speech Enhancement." IEEE Access 10 (2022): 14698–716. http://dx.doi.org/10.1109/access.2022.3147514.

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S, Rathnakara, and V. Udayashankara. "Enhancement of Speech Signal using Improved NLMS Algorithm." Communications on Applied Electronics 6, no. 9 (April 24, 2017): 34–37. http://dx.doi.org/10.5120/cae2017652557.

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Park, Sunho, and Seungjin Choi. "A constrained sequential EM algorithm for speech enhancement." Neural Networks 21, no. 9 (November 2008): 1401–9. http://dx.doi.org/10.1016/j.neunet.2008.03.001.

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Chen, Zi Qin, De Xiang Zhang, and Da Ling Yuan. "Speech Enhancement Based on EMD and Wavelet Threshold in Noisy Environments." Advanced Materials Research 989-994 (July 2014): 3654–57. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3654.

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Speech enhancement is crucial for speech recognition accuracy. How to eliminate the effect of the noise constitutes a challenging problem in speech processing. This paper presents a new technique for speech enhancement in a noisy environment based on the empirical mode decomposition (EMD) algorithm and wavelet threshold. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then wavelet threshold of the IMF components can be used to eliminate the effect of the noise for speech enhancement. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible.

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Ribas, Dayana, Antonio Miguel, Alfonso Ortega, and Eduardo Lleida. "Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement." Applied Sciences 12, no. 18 (September 7, 2022): 9000. http://dx.doi.org/10.3390/app12189000.

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This paper proposes a Deep Learning (DL) based Wiener filter estimator for speech enhancement in the framework of the classical spectral-domain speech estimator algorithm. According to the characteristics of the intermediate steps of the speech enhancement algorithm, i.e., the SNR estimation and the gain function, there is determined the best usage of the network at learning a robust instance of the Wiener filter estimator. Experiments show that the use of data-driven learning of the SNR estimator provides robustness to the statistical-based speech estimator algorithm and achieves performance on the state-of-the-art. Several objective quality metrics show the performance of the speech enhancement and beyond them, there are examples of noisy vs. enhanced speech available for listening to demonstrate in practice the skills of the method in simulated and real audio.

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LIN, CHIN-TENG, RUI-CHENG WU, and GIN-DER WU. "NOISY SPEECH SEGMENTATION/ENHANCEMENT WITH MULTIBAND ANALYSIS AND NEURAL FUZZY NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 927–55. http://dx.doi.org/10.1142/s0218001402002076.

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This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.

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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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Milivojevic, Zoran, and Dragisa Balaneskovic. "Enhancement of the perceptive quality of the noisy speech signal by using of DFF-FBC algorithm." Facta universitatis - series: Electronics and Energetics 22, no. 3 (2009): 391–404. http://dx.doi.org/10.2298/fuee0903391m.

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This paper presents an algorithm for enhancement of the noisy speech signal quality. This algorithm is based on the dissonant frequency filtering (DFF), F#, B and C# in relation to the frequency of the primary tone C (DFF-FBC algorithm). By means of the subjective Mean Opinion Score (MOS) test, the effect of the enhancement of the speech signal quality was analyzed. The analysis of the MOS test results, presented in the second part of this paper, points out to the enhancement of the noisy speech signal quality in the presence of superimposed noises. Especially good results have been found with Husky Voice signal. .

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Tantibundhit, C., F. Pernkopf, and G. Kubin. "Joint Time–Frequency Segmentation Algorithm for Transient Speech Decomposition and Speech Enhancement." IEEE Transactions on Audio, Speech, and Language Processing 18, no. 6 (August 2010): 1417–28. http://dx.doi.org/10.1109/tasl.2009.2035037.

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Jiang, Yi, Yuan Yuan Zu, and Ying Ze Wang. "An Unsupervised Approach to Close-Talk Speech Enhancement." Applied Mechanics and Materials 614 (September 2014): 363–66. http://dx.doi.org/10.4028/www.scientific.net/amm.614.363.

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A K-means based unsupervised approach to close-talk speech enhancement is proposed in this paper. With the frame work of computational auditory scene analysis (CASA), the dual-microphone energy difference (DMED) is used as the cue to classify the noise domain time-frequency (T-F) units and target speech domain units. A ratio mask is used to separate the target speech and noise. Experiment results show the robust performance of the proposed algorithm than the Wiener filtering algorithm.

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Sulong, Amart, Teddy Surya Gunawan, Othman O. Khalifa, Mira Kartiwi, and Hassan Dao. "Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 1941. http://dx.doi.org/10.11591/ijece.v7i4.pp1941-1951.

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<table width="593" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p class="Text">The speech enhancement algorithms are utilized to overcome multiple limitation factors in recent applications such as mobile phone and communication channel. The challenges focus on corrupted speech solution between noise reduction and signal distortion. We used a modified Wiener filter and compressive sensing (CS) to investigate and evaluate the improvement of speech quality. This new method adapted noise estimation and Wiener filter gain function in which to increase weight amplitude spectrum and improve mitigation of interested signals. The CS is then applied using the gradient projection for sparse reconstruction (GPSR) technique as a study system to empirically investigate the interactive effects of the corrupted noise and obtain better perceptual improvement aspects to listener fatigue with noiseless reduction conditions. The proposed algorithm shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at various noise type conditions of 0, 5, 10, 15 dB SNRs. Therefore, the proposed algorithm significantly achieved the speech quality improvement and efficiently obtained higher performance resulting in better noise reduction compare to other conventional algorithms. </p></td></tr></tbody></table>

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Tao, Tao, Hong Zheng, Jianfeng Yang, Zhongyuan Guo, Yiyang Zhang, Jiahui Ao, Yuao Chen, Weiting Lin, and Xiao Tan. "Sound Localization and Speech Enhancement Algorithm Based on Dual-Microphone." Sensors 22, no. 3 (January 18, 2022): 715. http://dx.doi.org/10.3390/s22030715.

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In order to simplify the complexity and reduce the cost of the microphone array, this paper proposes a dual-microphone based sound localization and speech enhancement algorithm. Based on the time delay estimation of the signal received by the dual microphones, this paper combines energy difference estimation and controllable beam response power to realize the 3D coordinate calculation of the acoustic source and dual-microphone sound localization. Based on the azimuth angle of the acoustic source and the analysis of the independent quantity of the speech signal, the separation of the speaker signal of the acoustic source is realized. On this basis, post-wiener filtering is used to amplify and suppress the voice signal of the speaker, which can help to achieve speech enhancement. Experimental results show that the dual-microphone sound localization algorithm proposed in this paper can accurately identify the sound location, and the speech enhancement algorithm is more robust and adaptable than the original algorithm.

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Nimmagadda, Padmaja, Kondru Ayyappa Swamy, Samuda Prathima, Sushma Chintha, and Zachariah Callottu Alex. "Short-term uncleaned signal to noise threshold ratio based end-to-end time domain speech enhancement in digital hearing aids." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (July 1, 2022): 131. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp131-138.

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This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the back ground noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech signal. The performance of the proposed algorithm is evaluated for speech signal with seven ty pes of noises and three signal to noise ratios (SNR) levels in each type of noise. From the results, it is clear that the basic level of adaptive speech enhancement is obtained using statistical parameters of noisy speech without the need for reference input.

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Xue, Hui Jun, Sheng Li, Teng Jiao, Guo Hua Lu, Yang Zhang, Jian Qi Wang, and Xi Jing Jing. "A Non-Contact Speech Enhancement Algorithm Based on Wavelet Packet Adaptive Threshold." Applied Mechanics and Materials 241-244 (December 2012): 194–98. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.194.

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Speech is an important method for human communication. In this paper, we developed a new method for detecting speech signal. Because of the advantage of this speech detecting method, it has great potential application value in many fields. Simultaneously, basing on the good capability of wavelet packet for analyzing time-frequency signal, this paper also developed an algorithm of wavelet packet threshold by using hard threshold and soft threshold for removing noise. Comparing to spectral subtraction and Wiener filter speech enhancement algorithm, the proposed algorithm takes on a better performance on noise removing and speech signal reserving.

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Dai, Peng, Ing Yann Soon, and Rui Tao. "Direct Recovery of Clean Speech Using a Hybrid Noise Suppression Algorithm for Robust Speech Recognition System." ISRN Signal Processing 2012 (December 26, 2012): 1–9. http://dx.doi.org/10.5402/2012/306305.

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A new log-power domain feature enhancement algorithm named NLPS is developed. It consists of two parts, direct solution of nonlinear system model and log-power subtraction. In contrast to other methods, the proposed algorithm does not need prior speech/noise statistical model. Instead, it works by direct solution of the nonlinear function derived from the speech recognition system. Separate steps are utilized to refine the accuracy of estimated cepstrum by log-power subtraction, which is the second part of the proposed algorithm. The proposed algorithm manages to solve the speech probability distribution function (PDF) discontinuity problem caused by traditional spectral subtraction series algorithms. The effectiveness of the proposed filter is extensively compared using the standard database, AURORA2. The results show that significant improvement can be achieved by incorporating the proposed algorithm. The proposed algorithm reaches a recognition rate of over 86% for noisy speech (average from SNR 0 dB to 20 dB), which means a 48% error reduction over the baseline Mel-frequency Cepstral Coefficient (MFCC) system.

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Journal articles on the topic 'Speech enhancement algorithm'

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