Results 141 to 150 of about 3,463 (178)
Explainable ResNet-long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion. [PDF]
J Int Med ResZhang W +12 more
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Advanced feature selection and temporal attention mechanisms with Bi-LSTM classifier for optimizing emotion recognition in Kashmiri speech. [PDF]
Front Artif IntellDar GM, Delhibabu R.
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Robust Heart Sound Analysis With MFCC and Light Weight Convolutional Neural Network. [PDF]
IEEE Open J Eng Med BiolHasan A, Karim M.
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The Utility of Speech and Language Analytics for Screening Alzheimer's Disease.
Neurodegener DisSiddiqui A +6 more
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2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2018
In this paper a comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an automatic bird species recognition system is proposed with the aim to validate IMFCC as a feature that can also be extracted for bird species recognition.
Aldonso Becerra
exaly +3 more sources
In this paper a comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an automatic bird species recognition system is proposed with the aim to validate IMFCC as a feature that can also be extracted for bird species recognition.
Aldonso Becerra
exaly +3 more sources
A mathematical relationship between full-band and multiband mel-frequency cepstral coefficients
IEEE Signal Processing Letters, 2002Recently, it has been shown that robustness of automatic speech recognition (ASR) against band-limited additive noises may be improved by multiband ASR (MBASR) approaches. In an M-subband MBASR system, the channels in the full-band filterbank are divided into M subbands, usually of equal partitions, and subband mel-frequency cepstral coefficients ...
B Mak
exaly +3 more sources
2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016
Speech processing is emerged as one of the important application area of digital signal processing. Power Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficient (MFCC) are mainly used in feature extraction of speech signals.
null Bharathi +2 more
openaire +1 more source
Speech processing is emerged as one of the important application area of digital signal processing. Power Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficient (MFCC) are mainly used in feature extraction of speech signals.
null Bharathi +2 more
openaire +1 more source
On the inversion of Mel-frequency cepstral coefficients for speech enhancement applications
2008 International Conference on Signals and Electronic Systems, 2008The use of Mel-frequency cepstral coefficients (MFCCs) is well established in the fields of speech processing, particularly for speaker modeling within a Gaussian mixture model (GMM) speaker recognition system. The use of GMMs for speech enhancement applications has only recently been proposed in the literature; the concept of direct inversion of the ...
Laura E. Boucheron, Phillip L. De Leon
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Mel-frequency cepstral coefficients as features for automatic speaker recognition
2015 23rd Telecommunications Forum Telfor (TELFOR), 2015Automatic speaker recognizer can be based on the use of mel-frequency cepstral coefficients as speaker features. Mel-frequency cepstral coefficients depend on energy inside considered auditory critical bands. These auditory critical bands model masking phenomena.
Ivan D. Jokic +3 more
openaire +1 more source