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Full-Text Articles in Signal Processing
Perceptually Motivated Wavelet Packet Transform For Bioacoustic Signal Enhancement, Yao Ren, Michael T. Johnson, Jidong Tao
Perceptually Motivated Wavelet Packet Transform For Bioacoustic Signal Enhancement, Yao Ren, Michael T. Johnson, Jidong Tao
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical …
Stress And Emotion Classification Using Jitter And Shimmer Features, Xi Li, Jidong Tao, Michael T. Johnson, Joseph Soltis, Anne Savage, Kirsten Leong, John D. Newman
Stress And Emotion Classification Using Jitter And Shimmer Features, Xi Li, Jidong Tao, Michael T. Johnson, Joseph Soltis, Anne Savage, Kirsten Leong, John D. Newman
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency cepstral coefficients (MFCCs) for human speech and Greenwood function cepstral coefficients (GFCCs) for animal vocalizations. Hidden Markov models (HMMs) with Gaussian mixture models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking …