IMPLEMENTATION OF MACHINE LEARNING AND DEEP LEARNING IN IMPROVING SIGNAL TO NOISE RATIO FOR AUDIO ANALYSIS IN DIGITAL MEDIA PLATFORMS

2020 ◽  
Author(s):  
SUDIPTA GHOSH
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Digital Media ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Audio Analysis ◽  
Noise Ratio

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

scholarly journals Digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection

2021 ◽  
Vol 18 (5) ◽  
pp. 6771-6789
Author(s):  
Hongyan Xu ◽  
Keyword(s):  
Edge Detection ◽  
Digital Media ◽  
Copyright Protection ◽  
Detection Rate ◽  
Signal To Noise Ratio ◽  
Edge Computing ◽  
Absolute Difference ◽  
Watermark Image ◽  
Signal To Noise ◽  
Noise Ratio

<abstract> <p>With the rapid development of computer technology and network communication technology, copyright protection caused by widely spread digital media has become the focus of attention in various fields. For digital media watermarking technology research emerge in endlessly, but the results are not ideal. In order to better realize the copyright identification and protection, based on the embedded intelligent edge computing detection technology, this paper studies the zero watermark copyright protection algorithm of digital media. Firstly, this paper designs an embedded intelligent edge detection module based on Sobel operator, including image line buffer module, convolution calculation module and threshold processing module. Then, based on the embedded intelligent edge detection module, the Arnold transform of image scrambling technology is used to preprocess the watermark, and finally a zero watermark copyright protection algorithm is constructed. At the same time, the robustness of the proposed algorithm is tested. The image is subjected to different proportion of clipping and scaling attacks, different types of noise, sharpening and blur attacks, and the detection rate and signal-to-noise ratio of each algorithm are calculated respectively. The performance of the watermark image processed by this algorithm is evaluated subjectively and objectively. Experimental data show that the detection rate of our algorithm is the highest, which is 0.89. In scaling attack, the performance of our algorithm is slightly lower than that of Fourier transform domain algorithm, but it is better than the other two algorithms. The Signal to Noise Ratio of the algorithm is 36.854% in P6 multiplicative noise attack, 39.638% in P8 sharpening edge attack and 41.285% in fuzzy attack. This shows that the algorithm is robust to conventional attacks. The subjective evaluation of 33% and 39% of the images is 5 and 4. The mean values of signal to noise ratio, peak signal to noise ratio, mean square error and mean absolute difference are 20.56, 25.13, 37.03 and 27.64, respectively. This shows that the watermark image processed by this algorithm has high quality. Therefore, the digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection is more robust, and its watermark invisibility is also very superior, which is worth promoting.</p> </abstract>

scholarly journals A Blind Spectrum Sensing Method Based on Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2270 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Xueqiong Li
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Spectrum Sensing ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Noise Ratio

Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.

A self-supervised Deep Learning approach for improving signal coherence in Distributed Acoustic Sensing

2021 ◽  
Author(s):  
Martijn van den Ende ◽  
Itzhak Lior ◽  
Jean Paul Ampuero ◽  
Anthony Sladen ◽  
Cédric Richard
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Direction Of Arrival ◽  
Fibre Optic ◽  
Coherent Signals ◽  
Signal To Noise ◽  
Coherent Signal ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing ◽  
Noise Ratio

&lt;p&gt;Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis as well as in monitoring of urban and marine environments, including microseismicity detection, ambient noise tomography, traffic density monitoring, and maritime vessel tracking. A major advantage of DAS is its ability to turn fibre-optic cables into large and dense seismic arrays. As a cornerstone of seismic array analysis, beamforming relies on the relative arrival times of coherent signals along the optical fibre array to estimate the direction-of-arrival of the signals, and can hence be used to locate earthquakes as well as moving acoustic sources (e.g. maritime vessels). Naturally, this technique can only be applied to signals that are sufficiently coherent in space and time, and so beamforming benefits from signal processing methods that enhance the signal-to-noise ratio of the spatio-temporally coherent signal components. DAS measurements often suffer from waveform incoherence, and processing submarine DAS data is particularly challenging.&lt;/p&gt;&lt;p&gt;In this work, we adopt a self-supervised deep learning algorithm to extract locally-coherent signal components. Owing to the similarity of coherent signals along a DAS system, one can predict the coherent part of the signal at a given channel based on the signals recorded at other channels, referred to as &quot;J-invariance&quot;. Following the recent approach proposed by Batson &amp; Royer (2019), we leverage the J-invariant property of earthquake signals recorded by a submarine fibre-optic cable. A U-net auto-encoder is trained to reconstruct the earthquake waveforms recorded at one channel based on the waveforms recorded at neighbouring channels. Repeating this procedure for every measurement location along the cable yields a J-invariant reconstruction of the dataset that maximises the local coherence of the data. When we apply standard beamforming techniques to the output of the deep learning model, we indeed obtain higher-fidelity estimates of the direction-of-arrival of the seismic waves, and spurious solutions resulting from a lack of waveform coherence and local seismic scattering are suppressed.&lt;/p&gt;&lt;p&gt;While the present application focuses on earthquake signals, the deep learning method is completely general, self-supervised, and directly applicable to other DAS-recorded signals. This approach facilitates the analysis of signals with low signal-to-noise ratio that are spatio-temporally coherent, and can work in tandem with existing time-series analysis techniques.&lt;/p&gt;&lt;p&gt;References:&lt;br&gt;Batson J., Royer L. (2019), &quot;Noise2Self: Blind Denoising by Self-Supervision&quot;, Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California&lt;/p&gt;

Micro-crack detection method of steel beam surface using stacked autoencoders on massive full-scale sensing strains

2019 ◽  
Vol 19 (4) ◽  
pp. 1175-1187 ◽  
Author(s):  
Qingsong Song ◽  
Yu Chen ◽  
Elias Abdoli Oskoui ◽  
Zheng Fang ◽  
Todd Taylor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Full Scale ◽  
Signal To Noise ◽  
High Quality ◽  
Strain Measurements ◽  
Feature Representations ◽  
Noise Ratio

Accurate micro-crack detections on the whole surface of civil structures have great significance. Distributed optical fiber sensor based on Brillouin optical time-domain analysis technology exhibits great facility to measure strain distributions along the whole surface of structures with a high spatial resolution, thus providing a potential and competitive solution to the detection problem. However, mainly due to low signal-to-noise ratio in measurements, such sensor system is still limited in crack detection–based structural health monitoring applications. How to extract high-quality micro-crack feature representations from the low signal-to-noise ratio–distributed strain measurements is crucial to solve the problem. It has been demonstrated in field of pattern recognition that deep learning can automatically extract high-quality noise-robust feature representations from mass chaos data. Therefore, a micro-crack detection method is proposed herein based on deep learning to analyze the full-scale strain measurements. Each measurement is normalized and segmented into a set of equal-length subsequences. Autoencoders, a typical kind of building block of deep neural network, are stacked layer-wise into a deep network and then exploited to automatically extract feature representations from the subsequences. Each extracted feature representation is labeled as one of the two categories by a Softmax regression. One category originates in the subsequences acquired from structure sections with crack defects and another from sections without any cracks. The micro-crack detections are achieved by solving such a crack/non-crack binary classification problem. A 15-m-long steel I-beam with artifact crack defects is built up in laboratory to verify the proposed method. Experimental results demonstrate that the minimum size of detectable crack opening width reaches to 23 μm, and besides, the proposed method is significantly better than traditional Fisher linear discriminant analysis method and classical support vector machine on the detection accuracy.

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