Prediction of cognitive impairment through speech data analysis: A comparative evaluation of deep learning models. [PDF]
PLoS OneKim M +8 more
europepmc +1 more source
Material Classification from Non-Line-of-Sight Acoustic Echoes Using Wavelet-Acoustic Hybrid Feature Fusion. [PDF]
Sensors (Basel)Alakuş DO, Türkoğlu İ.
europepmc +1 more source
Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls. [PDF]
Sci RepIdrisoglu A, Behrens A.
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Mel-frequency Cepstral Coefficients for Eye Movement Identification
2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012 Human identification is an important task for various activities in society. In this paper, we consider the problem of human identification using eye movement information. This problem, which is usually called the eye movement identification problem, can be solved by training a multiclass classification model to predict a person's identity from his or ...
Viet Cuong Nguyen +2 more
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Mel Frequency Cepstral Coefficients and Support Vector Machines for Cough Detection
Lecture Notes in Computer Science, 2023Dustin Van Der Haar +2 more
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Structural Health Monitoring, 2023 Bridge damage detection using vibration data has been confirmed as a promising approach. Compared to the traditional method that typically needs to install sensors or systems directly on bridges, the drive-by bridge damage detection method has gained ...
Weiwei Lin, Zhenkun Li, Youqi Zhang
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Computing Mel-frequency cepstral coefficients on the power spectrum
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002We present a method to derive Mel-frequency cepstral coefficients directly from the power spectrum of a speech signal. We show that omitting the filterbank in signal analysis does not affect the word error rate. The presented approach simplifies the speech recognizers front end by merging subsequent signal analysis steps into a single one.
Sirko Molau +3 more
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A mathematical relationship between full-band and multiband mel-frequency cepstral coefficients
IEEE Signal Processing Letters, 2002 Recently, 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
B Mak
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Chip design of mel frequency cepstral coefficients for speech recognition
2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002The mel frequency cepstral coefficients (MFCC) is one of the mast important features, which is required among various kinds of speech applications. The chip for speech features extraction based on the MFCC algorithm is first proposed. The chip is designed with area efficient consideration and can achieve the following: (1) the reduction of table size ...
Jia-Ching Wang +2 more
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Minimum Mean-Squared Error Estimation of Mel-Frequency Cepstral Coefficients Using a Novel Distortion Model [PDF]
IEEE Transactions on Audio Speech and Language Processing, 2008 In this paper, a new method for statistical estimation of Mel-frequency cepstral coefficients (MFCCs) in noisy speech signals is proposed. Previous research has shown that model-based feature domain enhancement of speech signals for use in robust speech ...
Michael Johnson, Richard Povinelli
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