Pink noise generation using wavelets

Isar, Dorina; Gajitzki, Paul

doi:10.1109/isetc.2016.7781107

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…One of them is to get pink noise with given parameters. Pink noise, also known as 1/f noise, is a random stochastic process or a signal whose mean power spectral density is inversely proportional to frequency (Isar and Gajitzki, 2016). According to Voytek et al (2015), many natural phenomena, including electroencephalography, can be described by 1/f noise.…”

Section: Generated Electroencephalography/ Electrooculography Signalsmentioning

confidence: 99%

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

2022

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Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.

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Section: Generated Electroencephalography/ Electrooculography Signalsmentioning

confidence: 99%

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

2022

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“…• White noise: as pointed out by [31], happens when "each audible frequency is equally loud," meaning no sound feature, "shape or form can be distinguished"; • Gaussian noise: This noise can arise in amplifiers or detectors, having a probability density function that is proximal to real world scenarios [87]. Gaussian noise is noise distributed in a normal, bell-shaped like fashion; • Pink noise: also known as flicker noise, is a random process with an average power spectral density inversely proportional to the frequency of the input signal [45];…”

Section: Evasion Attacksmentioning

confidence: 99%

Audio Attacks and Defenses against AED Systems -- A Practical Study

Santos¹,

Nilizadeh²

2021

Preprint

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Audio Event Detection (AED) Systems capture audio from the environment and employ some deep learning algorithms for detecting the presence of a specific sound of interest. In this paper, we evaluate deep learning-based AED systems against evasion attacks through adversarial examples. We run multiple security critical AED tasks, implemented as CNNs classifiers, and then generate audio adversarial examples using two different types of noise, namely background and white noise, that can be used by the adversary to evade detection. We also examine the robustness of existing third-party AED capable devices, such as Nest devices manufactured by Google, which run their own black-box deep learning models.We show that an adversary can focus on audio adversarial inputs to cause AED systems to misclassify, similarly to what has been previously done by works focusing on adversarial examples from the image domain. We then, seek to improve classifiers' robustness through countermeasures to the attacks. We employ adversarial training and a custom denoising technique. We show that these countermeasures, when applied to audio input, can be successful, either in isolation or in combination, generating relevant increases of nearly fifty percent in the performance of the classifiers when these are under attack.

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“…Pink Noise is one of the noise that causes disturbance in the speech signal [10]. It is also called as 1/f noise because of its power spectral density is inversely proportional to frequency.…”

Section: E Properties Of Pink Noise That Gets Addedmentioning

confidence: 99%

Removal of Pink Noise from Corrupted Speech Signal using Kalman Filter

Ahmed*,

Hota

2020

IJITEE

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Speech enhancement has been a major challenge in the field of Signal processing. The process of filtering the noise component from the speech signal has achieved many milestones since the early 20th century. Beside many theories Linear prediction coding is one of the best methods for speech, audio signal processing which uses the algorithm of predicting the current estimates based on the past states of an LTI system. Linear prediction is usually used in Speech recognition, Speech enhancement. One of such Kalman filter was introduced and described in 1960 by Rudolf Kalman, which uses the concept of linear quadratic estimation. Kalman filtering is effectively being used in the practical applications like navigation of ships or aircraft, designing motion planning algorithms, in communication area. Kalman filters use the autoregression model of speech for the recursive equations of Kalman filter used in state space model of filter for state estimation. In this paper, we have used Kalman filter to eliminate the pink noise from the corrupted speech signal. Pink noise is very common in electronic devices and occurs in almost all devices. The Speech corrupted with pink noise has been obtained from SpEAR database. We have used MATLAB software for the simulation purpose. Finally, Spectrograms of signals are plotted for a better visual understanding of filtered results.

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Pink noise generation using wavelets

Cited by 5 publications

References 10 publications

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

Audio Attacks and Defenses against AED Systems -- A Practical Study

Removal of Pink Noise from Corrupted Speech Signal using Kalman Filter

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