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Proceedings ArticleDOI: 10.1109/ICICI.2017.8365318

Efficiency analysis of noise reduction algorithms: Analysis of the best algorithm of noise reduction from a set of algorithms

01 Nov 2017-
Abstract: For greater advancement in future communication, efficient noise reduction algorithms with lesser complexity are a necessity. Noise in audio signal poses a great challenge in speech recognition, speech communication, speech enhancement and transmission. Hence the most efficient algorithm for noise reduction must be chosen in such a way that the cost for noise removal is a less as possible, but a large portion of noise is removed. The common method for the removal of noise is optimal linear filtering method, and some algorithms in this method are Wiener filtering, Kalman filtering and spectral subtraction technique. Here, the noise signal is passed through a filter or transformation. However, due to the complexity of these algorithms, there are better algorithms like Signal Dependent Rank Order Mean algorithm (SD-ROM), which removes noise from audio signals and retains the characteristics of the signal. The algorithm can be adjusted depending on the characteristics of noise signal too. To remove white Gaussian noise, discrete wavelet transform technique is used. After each of the techniques are applied to the samples, SNR and elapsed time are calculated. All of the above techniques show an increased Signal to Noise Ratio (SNR) after processing, as seen in the simulation results.

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Topics: Noise (signal processing) (73%), Signal-to-noise ratio (67%), Noise reduction (66%) ...read more
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Proceedings ArticleDOI: 10.1109/ICSCCC.2018.8703305
01 Dec 2018-
Abstract: In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches.

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Topics: Gaussian noise (72%), Image noise (72%), Median filter (70%) ...read more

4 Citations


Open accessJournal ArticleDOI: 10.32628/IJSRST218342
Abstract: Podcast allows recording of audio file on any topic. It is like an interview where one person questions and another person replies back to that question. Podcasting requires both the people to be present physically at one location. But recently, due to the pandemic, it is conducted online which most of the time results in poor quality of the audio. One of the reasons for the same is the presence of noise in it. This paper compares various noise reduction algorithms and also states the best algorithm to solve the above problem. We carried out our experiment on various audio files. Results were compared against various noise reduction methods and best ratio was obtained for Spectral Gating Algorithm.

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Topics: Noise reduction (60%)

Proceedings ArticleDOI: 10.1109/BIBE.2019.00083
Elhoussine Talab1, Omar Mohamed1, Labeeba Begum1, Fadi Aloul1  +1 moreInstitutions (1)
01 Oct 2019-
Abstract: One out of four deaths is caused by heart related issues. Acting upon early signs of heart disease can, thus, drastically increase probability of saving lives. This paper discusses a cost-effective and reliable method of diagnosing heart abnormalities by using mobile phones that are nowadays typically available to an average user. A mobile application is developed to detect heart abnormal activities using either a digital stethoscope measurement as input, or a mobile recording of the heart beat using the mobile's microphone. To process the raw heart sound data, we first denoise the signal using wavelet transforms, and then apply machine learning techniques, namely, Convolutional Neural Networks for the classification of the stored heart sounds. A database consisting of recorded human heart sounds and their corresponding diagnosis is used to train the neural network. Moreover, neural network fine-tuning techniques such as ADAM Regularization is used to smoothen the prediction process. The proposed approach is tested on heart sound signals, that are 5 to 8 seconds long, and is shown to perform with an accuracy of 94.2% on the validation set.

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Topics: Heart malformation (64%), Heart sounds (62%), Stethoscope (53%) ...read more

Book ChapterDOI: 10.1007/978-981-33-6881-1_4
01 Jan 2021-
Abstract: As there is aggrandizement in the sector of artificial intelligence relating to the speech domain, it becomes a necessity to have efficient noise removal models with greater efficiency and less complexity. The presence of noise in audio signals poses a great complication when working on speech recognition, enhancement, improvement, and transmission. Hence, there is a necessity to develop the most efficient algorithm for noise reduction which works in real time and is successful in removing maximum noise. To be above this difficulty, this paper presents an efficient algorithm for noise detection which works on the principles of deep learning, specifically convolutional neural networks (CNNs) and the removal of similar noise from the audio using the Python module ‘noise reducer.’

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Topics: Noise (66%), Noise reduction (59%), Audio signal (58%) ...read more
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