87,272 research outputs found
Study of Fingerprint Enhancement and Matching
Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
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