Relevant bibliographies by topics / Cepstral Mean Subtraction

Academic literature on the topic 'Cepstral Mean Subtraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Cepstral Mean Subtraction"

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Yang, Pu, Yingchun Yang, and Zhaohui Wu. "Robust speaker recognition using glottal information‐based cepstral mean subtraction." Journal of the Acoustical Society of America 116, no. 4 (October 2004): 2481–82. http://dx.doi.org/10.1121/1.4784912.

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Shabtai, Noam R., Boaz Rafaely, and Yaniv Zigel. "The effect of reverberation on the performance of cepstral mean subtraction in speaker verification." Applied Acoustics 72, no. 2-3 (February 2011): 124–26. http://dx.doi.org/10.1016/j.apacoust.2010.09.009.

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Veisi, Hadi, and Hossein Sameti. "The integration of principal component analysis and cepstral mean subtraction in parallel model combination for robust speech recognition." Digital Signal Processing 21, no. 1 (January 2011): 36–53. http://dx.doi.org/10.1016/j.dsp.2010.07.004.

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Boulmaiz, Amira, Djemil Messadeg, Noureddine Doghmane, and Abdelmalik Taleb-Ahmed. "Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species using TMS320C6713 DSK." International Journal of Ambient Computing and Intelligence 8, no. 1 (January 2017): 98–118. http://dx.doi.org/10.4018/ijaci.2017010105.

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In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.
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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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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 Subtraction"

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Neville, Katrina Lee, and katrina neville@rmit edu au. "Channel Compensation for Speaker Recognition Systems." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080514.093453.

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This thesis attempts to address the problem of how best to remedy different types of channel distortions on speech when that speech is to be used in automatic speaker recognition and verification systems. Automatic speaker recognition is when a person's voice is analysed by a machine and the person's identity is worked out by the comparison of speech features to a known set of speech features. Automatic speaker verification is when a person claims an identity and the machine determines if that claimed identity is correct or whether that person is an impostor. Channel distortion occurs whenever information is sent electronically through any type of channel whether that channel is a basic wired telephone channel or a wireless channel. The types of distortion that can corrupt the information include time-variant or time-invariant filtering of the information or the addition of 'thermal noise' to the information, both of these types of distortion can cause varying degrees of error in information being received and analysed. The experiments presented in this thesis investigate the effects of channel distortion on the average speaker recognition rates and testing the effectiveness of various channel compensation algorithms designed to mitigate the effects of channel distortion. The speaker recognition system was represented by a basic recognition algorithm consisting of: speech analysis, extraction of feature vectors in the form of the Mel-Cepstral Coefficients, and a classification part based on the minimum distance rule. Two types of channel distortion were investigated: • Convolutional (or lowpass filtering) effects • Addition of white Gaussian noise Three different methods of channel compensation were tested: • Cepstral Mean Subtraction (CMS) • RelAtive SpecTrAl (RASTA) Processing • Constant Modulus Algorithm (CMA) The results from the experiments showed that for both CMS and RASTA processing that filtering at low cutoff frequencies, (3 or 4 kHz), produced improvements in the average speaker recognition rates compared to speech with no compensation. The levels of improvement due to RASTA processing were higher than the levels achieved due to the CMS method. Neither the CMS or RASTA methods were able to improve accuracy of the speaker recognition system for cutoff frequencies of 5 kHz, 6 kHz or 7 kHz. In the case of noisy speech all methods analysed were able to compensate for high SNR of 40 dB and 30 dB and only RASTA processing was able to compensate and improve the average recognition rate for speech corrupted with a high level of noise (SNR of 20 dB and 10 dB).
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Book chapters on the topic "Cepstral Mean Subtraction"

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Boulmaiz, Amira, Djemil Messadeg, Noureddine Doghmane, and Abdelmalik Taleb-Ahmed. "Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species Using TMS320C6713 DSK." In Sensor Technology, 800–821. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch038.

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Abstract:
In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.
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Conference papers on the topic "Cepstral Mean Subtraction"

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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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Radadia, Purushotam G., and Hemant A. Patil. "A Cepstral Mean Subtraction based features for Singer Identification." In 2014 International Conference on Asian Language Processing (IALP). IEEE, 2014. http://dx.doi.org/10.1109/ialp.2014.6973510.

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Du, Feifei, Qizhi Huang, Chengyuan Wei, and Bo Wang. "Speech Endpoint Detection Based on Improved Cepstral Mean Subtraction." In 2012 Second International Conference on Intelligent System Design and Engineering Application (ISDEA). IEEE, 2012. http://dx.doi.org/10.1109/isdea.2012.521.

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