Relevant bibliographies by topics / Cepstral Mean Normalization (CMN)

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

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)"

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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
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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 s
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Zgank, Andrej. "Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service." Sensors 20, no. 1 (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
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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
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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 (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 simul
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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 (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 M
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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 (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 cep
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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 (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
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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
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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 (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, 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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