Dictionary Learning with Applications to Audio Signals Год выпуска: 2014Автор: Daniele BarchiesiИздательство: Scholars PressСтраниц: 196ISBN: 9783639666083Описание Over-complete transforms have recently become the focus of a wide wealth of research in signal processing, machine learning, statistics and related fields. Their great modelling flexibility allows to find sparse representations and approximations of data that in turn prove to be very efficient in a wide range of applications. Sparse models express signals as linear combinations of a few basis functions called atoms taken from a so-called dictionary. Finding the optimal dictionary from a set of training signals of a given class is the objective of dictionary learning and the main focus of this thesis. The experimental evidence presented here focuses on the processing of audio signals, and the role of sparse algorithms in audio applications is accordingly highlighted.
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Алина, 12.05 Добрый день! Марина, хочу поблагодарить Вас и Вашу команду (особенно Юлию) за проделанную работу! Курсовая работа принята и защищена с оценкой отлично. Спасибо Вам! Выражаю огромную благодарность за оперативную работу и понимание))
