Teager Energy Cepstral Coefficients for Classification of Normal vs. Whisper Speech

Khoria, Kuldeep; Kamble, Madhu R.; Patil, Hemant A.

doi:10.23919/eusipco47968.2020.9287634

Cited by 8 publications

(1 citation statement)

References 24 publications

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“…where, x is the sample window and n is the number of windows. TECCs are similar to mel-frequency cepstral coefficients [24] and consists of pre-processing, Gabor filterbank, TEO, framing, averaging, log, and discrete cosine transform along with cepstral mean subtraction phases [25]. Fig.…”

Section: Teager Energy Cepstral Coefficientsmentioning

confidence: 99%

Student’s Health Exercise Recognition Tool for E-Learning Education

Shloul¹,

Gochoo²,

Alsuhibany³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Due to the recently increased requirements of e-learning systems, multiple educational institutes such as kindergarten have transformed their learning towards virtual education. Automated student health exercise is a difficult task but an important one due to the physical education needs especially in young learners. The proposed system focuses on the necessary implementation of student health exercise recognition (SHER) using a modified Quaternion-based filter for inertial data refining and data fusion as the pre-processing steps. Further, cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal patterns. Furthermore, these patterns have been utilized to extract cues for both patterned signals, which are further optimized using Fisher's linear discriminant analysis (FLDA) technique. Finally, the physical exercise activities have been categorized using extended Kalman filter (EKF)-based neural networks. This system can be implemented in multiple educational establishments including intelligent training systems, virtual mentors, smart simulations, and interactive learning management methods.

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Section: Teager Energy Cepstral Coefficientsmentioning

confidence: 99%