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Relevant bibliographies by topics / Cepstral Mean Normalization (CMN)

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Academic literature on the topic 'Cepstral Mean Normalization (CMN)'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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Journal articles on the topic "Cepstral Mean Normalization (CMN)"

1

Yang, Jie. "Combining Speech Enhancement and Cepstral Mean Normalization for LPC Cepstral Coefficients." Key Engineering Materials 474-476 (April 2011): 349–54. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.349.

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A mismatch between the training and testing in noisy circumstance often causes a drastic decrease in the performance of speech recognition system. The robust feature coefficients might suppress this sensitivity of mismatch during the recognition stage. In this paper, we investigate the noise robustness of LPC Cepstral Coefficients (LPCC) by using speech enhancement with feature post-processing. At front-end, speech enhancement in the wavelet domain is used to remove noise components from noisy signals. This enhanced processing adopts the combination of discrete wavelet transform (DWT), wavelet packet decomposition (WPD), multi-thresholds processing etc to obtain the estimated speech. The feature post-processing employs cepstral mean normalization (CMN) to compensate the signal distortion and residual noise of enhanced signals in the cepstral domain. The performance of digit speech recognition systems is evaluated under noisy environments based on NOISEX-92 database. The experimental results show that the presented method exhibits performance improvements in the adverse noise environment compared with the previous features.

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Huang, Yi Bo, Qiu Yu Zhang, Zhan Ting Yuan, and Peng Fei Xing. "Speech Perception Hash Authentication Algorithm Based on Immittance Spectral Pairs." Applied Mechanics and Materials 610 (August 2014): 385–92. http://dx.doi.org/10.4028/www.scientific.net/amm.610.385.

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According to the situation that traditional speech authentication algorithms don’t be appropriated for present speech communication, we proposed a speech authentication algorithm of perceptual hashing based on Immittance Spectral Pairs. It can satisfy the requirement of the efficiency and the robustness for speech authentication. Firstly, the speech signal pre-processing, for framing, adding window, obtained for each speech frame immittance spectral Pairs parameters, constitute an immittance spectral Pairs parameter matrix. Then process cepstral mean and variance normalization for immittance spectral Pairs parameter matrix, cepstral mean and variance normalization can effectively improve the robustness of the Gaussian white noise. And parameter matrix for non-negative matrix factorization. Finally, quantifying the formed weight matrix and getting perceptual hashing sequences.Experiments show that the proposed algorithm has good robustness for content preserving operations, and it doesn’t reduce the efficiency while meeting robustness, it can satisfy the real-time requirement of speech communication.

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Zgank, Andrej. "Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service." Sensors 20, no. 1 (December 19, 2019): 21. http://dx.doi.org/10.3390/s20010021.

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Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.

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Kadhim, Samah Abdulridha Abdul, Fadhaa Abdulameer Ghafil, Sahar A. Majeed, and Najah R. Hadi. "NEPHROPROTECTIVE EFFECTS OF CURCUMIN AGAINST CYCLOSPORINE A-INDUCED NEPHROTOXICITY IN RAT MODEL." Wiadomości Lekarskie 74, no. 12 (2021): 3135–46. http://dx.doi.org/10.36740/wlek202112103.

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https://wiadlek.pl/wp-content/uploads/archive/2021/WLek2021121.pdf The aim: The current study was designed to examine the possible Nephroprotective effects of CMN in preventing nephrotoxicity and oxidative stress caused by chronic administration of CsA in rats. Materials and methods: This study consisted of four groups and each group was made up of 8 rats. The first group was considered as a control group (received vehicle (0.9%N/S orally, and olive oil S.C), and the rest included the following: CMN group (received CMN in a dose of 30mg/kg/day orally), CsA group (received CsA in a dose of 20mg/kg/day S.C), and CMN plus CsA combination group (received CMN (30mg/kg/day, orally) plus CsA (20mg/kg/day, S.C) for 21days). For each group, the following variables wereassessed: Serum urea concentration, Serum creatinine concentration, initial body weight, final body weight, Tissue MDA level, Tissue GpX1 level, Tissue CAT level, Tissue SOD level, and tissue IL-2 level, and histopathological examination. Results: Mean levels of serum urea and creatinine, tissue MDA, tissue IL-2, and histopathological scores are significantly (P<0.05) increased in the CsA group compared with the control, and CMN groups (normal renal tissue). Tissue SOD, CAT, and GpX1 activities are significantly (P<0.05) decreased in the CsA group compared with the control, and CMN group. Concomitant administration of CMN with CsA resulted in significantly (P<0.05) lower elevated levels of MDA, serum urea, and creatinine, significantly higher levels of antioxidant enzymes, and normalization of the altered renal morphology compared with CsA treated rats. Conclusions: CMN has antioxidant and anti-inflammatory properties that protect the kidney from CsA’s toxicity.

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Deng, Lei, and Yong Gao. "Gammachirp Filter Banks Applied in Roust Speaker Recognition Based GMM-UBM Classifier." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 170–77. http://dx.doi.org/10.34028/iajit/17/2/4.

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In this paper, authors propose an auditory feature extraction algorithm in order to improve the performance of the speaker recognition system in noisy environments. In this auditory feature extraction algorithm, the Gammachirp filter bank is adapted to simulate the auditory model of human cochlea. In addition, the following three techniques are applied: cube-root compression method, Relative Spectral Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal Background Model (GMM-UBM), the simulated experiment was conducted. The experimental results implied that speaker recognition systems with the new auditory feature has better robustness and recognition performance compared to Mel-Frequency Cepstral Coefficients(MFCC), Relative Spectral-Perceptual Linear Predictive (RASTA-PLP),Cochlear Filter Cepstral Coefficients (CFCC) and gammatone Frequency Cepstral Coefficeints (GFCC)

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Al-Kaltakchi, Musab T. S., Haithem Abd Al-Raheem Taha, Mohanad Abd Shehab, and Mohamed A. M. Abdullah. "Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 782. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp782-789.

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<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

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Amara korba, Mohamed Cherif, Houcine Bourouba, and Rafik Djemili. "FEATURE EXTRACTION ALGORITHM USING NEW CEPSTRAL TECHNIQUES FOR ROBUST SPEECH RECOGNITION." Malaysian Journal of Computer Science 33, no. 2 (April 24, 2020): 90–101. http://dx.doi.org/10.22452/mjcs.vol33no2.1.

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In this work, we propose a novel feature extraction algorithm that improves the robustness of automatic speech recognition (ASR) systems in the presence of various types of noise. The proposed algorithm uses a new cepstral technique based on the differential power spectrum (DPS) instead of the power spectrum (PS), the algorithm replaces the logarithmic non linearity by the power function. In order to reduce cepstral coefficients mismatches between training and testing conditions, we used the mean and variance normalization, then we apply auto-regression movingaverage filtering (MVA) in the cepstral domain. The ASR experiments were conducted using two databases, the first is LASA digit database designed for recognition the isolated Arabic digits in the presence of different types of noise. The second is Aurora 2 noisy speech database designed to recognize connected English digits in various operating environments. The experimental results show a substantial improvement from the proposed algorithm over the baseline Mel Frequency Cepstral Coefficients (MFCC), the relative improvement is the 28.92% for LASA database and is the 44.43% for aurora 2 database. The performance of our proposed algorithm was tested and verified by extensive comparisons with the state-of-the-art noise-robust features in aurora 2.

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Nawasta, Revanto Alif, Nur Heri Cahyana, and Heriyanto Heriyanto. "Implementation of Mel-Frequency Cepstral Coefficient as Feature Extraction using K-Nearest Neighbor for Emotion Detection Based on Voice Intonation." Telematika 20, no. 1 (March 1, 2023): 51. http://dx.doi.org/10.31315/telematika.v20i1.9518.

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Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN as an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts on YouTube. Some of the methods used in this study are pitch shifting for data augmentation, MFCC for feature extraction on audio data, basic statistics for taking the mean, median, min, max, standard deviation for each coefficient, Min max scaler for the normalization process and KNN for the method classification.Findings/result: Because testing is carried out separately for each gender, there are two classification models. In the male model, the highest accuracy was obtained at 88.8% and is included in the good fit model. In the female model, the highest accuracy was obtained at 92.5%, but the model was unable to correctly classify emotions in the new data. This condition is called overfitting. After testing, the cause of this condition was because the pitch shifting augmentation process of one tone in women was unable to solve the problem of the training data size being too small and not containing enough data samples to accurately represent all possible input data values.Originality/value/state of the art: The research data used in this study has never been used in previous studies because the research data is obtained by downloading from Youtube and then processed until the data is ready to be used for research.

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MISHRA, DEBASHISH DEV, UTPAL BHATTACHARJEE, and SHIKHAR KUMAR SARMA. "MFCC AND CMN BASED SPEAKER RECOGNITION IN NOISY ENVIRONMENT." International Journal of Electronics Signals and Systems, July 2013, 48–51. http://dx.doi.org/10.47893/ijess.2013.1137.

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The performance of automatic speaker recognition (ASR) system degrades drastically in the presence of noise and other distortions, especially when there is a noise level mismatch between the training and testing environments. This paper explores the problem of speaker recognition in noisy conditions, assuming that speech signals are corrupted by noise. A major problem of most speaker recognition systems is their unsatisfactory performance in noisy environments. In this experimental research, we have studied a combination of Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Cepstral Mean Normalization (CMN) techniques for speech enhancement. Our system uses a Gaussian Mixture Models (GMM) classifier and is implemented under MATLAB®7 programming environment. The process involves the use of speaker data for both training and testing. The data used for testing is matched up against a speaker model, which is trained with the training data using GMM modeling. Finally, experiments are carried out to test the new model for ASR given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system.

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Farahani, Gholamreza. "Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition." EURASIP Journal on Audio, Speech, and Music Processing 2017, no. 1 (June 21, 2017). http://dx.doi.org/10.1186/s13636-017-0110-8.

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Dissertations / Theses on the topic "Cepstral Mean Normalization (CMN)"

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Sujatha, J. "Improved MFCC Front End Using Spectral Maxima For Noisy Speech Recognition." Thesis, 2005. https://etd.iisc.ac.in/handle/2005/1506.

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Sujatha, J. "Improved MFCC Front End Using Spectral Maxima For Noisy Speech Recognition." Thesis, 2005. http://etd.iisc.ernet.in/handle/2005/1506.

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Conference papers on the topic "Cepstral Mean Normalization (CMN)"

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Naik, Devang K., and Richard J. Mammone. "Channel normalization using pole-filtered cepstral mean subtraction." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Richard J. Mammone and J. David Murley, Jr. SPIE, 1994. http://dx.doi.org/10.1117/12.191872.

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Kalinli, Ozlem, Gautam Bhattacharya, and Chao Weng. "Parametric Cepstral Mean Normalization for Robust Speech Recognition." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683674.

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Joshi, Vikas, N. Vishnu Prasad, and S. Umesh. "Modified cepstral mean normalization — transforming to utterance specific non-zero mean." In Interspeech 2013. ISCA: ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-260.

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Prasad, N. Vishnu, and S. Umesh. "Improved cepstral mean and variance normalization using Bayesian framework." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707722.

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Garcia, A. A., and R. J. Mammone. "Channel-robust speaker identification using modified-mean cepstral mean normalization with frequency warping." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758128.

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Baek, Soonho, and Hong-Goo Kang. "Mean normalization of power function based cepstral coefficients for robust speech recognition in noisy environment." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853895.

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Parssinen, Salmela, Harju, and Kiss. "Comparing Jacobian adaptation with cepstral mean normalization and parallel model combination for noise robust speech recognition." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005709.

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Parssinen, Kimmo, Petri Salmela, Mikko Harju, and Imre Kiss. "Comparing Jacobian adaptation with cepstral mean normalization and parallel model combination for noise robust speech recognition." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743687.

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