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Видалення імпульсного шуму з аудіосигналу

Видалення імпульсного шуму з аудіосигналу

Abstract

У роботі розглянуто методи, які використовуються для вирішення поставленої задачі. Проаналізовано їх архітектуру, принципи роботи, конкретні приклади, у яких вони використовувалися, їх переваги та недоліки. Описана задача виявлення та видалення імпульсного шуму в аудіосигналах з використанням методів машинного навчання. На основі проведеного дослідження запропоновано програмне рішення з використанням алгоритму спектрального віднімання, яке дозволяє виявляти та видаляти імпульсний шум в аудіосигналах з метою покращення якості сприйняття аудіо. The work examines the methods used to solve the given problem. Their architecture, principles of operation, specific examples in which they were used, their advantages and disadvantages were analyzed. The task of detecting and removing impulse noise in audio signals using machine learning methods is described. On the basis of the conducted research, a software solution using the spectral subtraction algorithm is proposed, which allows detecting and removing impulse noise in audio signals in order to improve the quality of audio perception.

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Keywords

видалення шуму, impulse noise, machine learning, audio signal, імпульсний шум, спектральне віднімання, аудіосигнал, spectral subtraction, машинне навчання, recognition, noise removal

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green