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- Agroacoustics (2)
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Articles 1 - 3 of 3
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 …
An Improved Snr Estimator For Speech Enhancement, Yao Ren, Michael T. Johnson
An Improved Snr Estimator For Speech Enhancement, Yao Ren, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
In this paper, we propose an MMSE a priori SNR estimator for speech enhancement. This estimator has similar benefits to the well-known decision-directed approach, but does not require an ad-hoc weighting factor to balance the past a priori SNR and current ML SNR estimate with smoothing across frames. Performance is evaluated in terms of estimation error and segmental SNR using the standard logSTSA speech enhancement method. Experimental results show that, in contrast with the decision-directed estimator and ML estimator, the proposed SNR estimator can help enhancement algorithms preserve more weak speech information and efficiently suppress musical noise.