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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 …
Acoustic Model Adaptation For Ortolan Bunting (Emberiza Hortulana L.) Song-Type Classification, Jidong Tao, Michael T. Johnson, Tomasz S. Osiejuk
Acoustic Model Adaptation For Ortolan Bunting (Emberiza Hortulana L.) Song-Type Classification, Jidong Tao, Michael T. Johnson, Tomasz S. Osiejuk
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
Automatic systems for vocalization classification often require fairly large amounts of data on which to train models. However, animal vocalization data collection and transcription is a difficult and time-consuming task, so that it is expensive to create large data sets. One natural solution to this problem is the use of acoustic adaptation methods. Such methods, common in human speech recognition systems, create initial models trained on speaker independent data, then use small amounts of adaptation data to build individual-specific models. Since, as in human speech, individual vocal variability is a significant source of variation in bioacoustic data, acoustic model adaptation …
Generalized Perceptual Linear Prediction (Gplp) Features For Animal Vocalization Analysis, Patrick J. Clemins, Michael T. Johnson
Generalized Perceptual Linear Prediction (Gplp) Features For Animal Vocalization Analysis, Patrick J. Clemins, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animalvocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equal loudness normalization into the feature extraction process. Since such perceptual information is available for a number of animal species, this new approach integrates that information into a generalized model to extract perceptually relevant features for a particular species. To illustrate, qualitative and quantitative comparisons are made …