scholarly journals Evaluation of Audio Compression Artifacts

10.14311/906 ◽  
2007 ◽  
Vol 47 (1) ◽  
Author(s):  
M. Herrera Martinez
Keyword(s):  
State Of The Art ◽  
Subjective Evaluation ◽  
Audio Coding ◽  
Coding System ◽  
Audio Compression ◽  
Coding Systems ◽  
Wide Range ◽  
Different Types ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal and the algorithm of the audio-coding system, different types of audible errors arise. These errors are called coding artifacts. Although three kinds of artifacts are perceivable in the auditory domain, the author proposes that in the coding domain there is only one common cause for the appearance of the artifact, inefficient tracking of transient-stochastic signals. For this purpose, state-of-the art audio coding systems use a wide range of signal processing techniques, including application of the wavelet transform, which is described here. 

scholarly journals A comprehensive study of the rate-distortion performance in MPEG point cloud compression

2019 ◽  
Vol 8 ◽  
Author(s):  
Evangelos Alexiou ◽  
Irene Viola ◽  
Tomás M. Borges ◽  
Tiago A. Fonseca ◽  
Ricardo L. de Queiroz ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Subjective Evaluation ◽  
Rate Distortion ◽  
Point Clouds ◽  
Current Approach ◽  
User Engagement ◽  
Viable Solution ◽  
Wide Range ◽  
Objective Quality

Abstract Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

scholarly journals Thresholded ConvNet ensembles: neural networks for technical forecasting

2020 ◽  
Vol 32 (18) ◽  
pp. 15249-15262
Author(s):  
Sid Ghoshal ◽  
Stephen Roberts
Keyword(s):  
Neural Networks ◽  
Domain Knowledge ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Financial Time Series ◽  
Visual Representations ◽  
Financial Time ◽  
Subjective Form ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of ‘domain knowledge’. We investigate the predictive value of patterns in financial time series, applying machine learning and signal processing techniques to 22 years of US equity data. By reframing technical analysis as a poorly specified, arbitrarily preset feature-extractive layer in a deep neural network, we show that better convolutional filters can be learned directly from the data, and provide visual representations of the features being identified. We find that an ensemble of shallow, thresholded convolutional neural networks optimised over different resolutions achieves state-of-the-art performance on this domain, outperforming technical methods while retaining some of their interpretability.

Noise types and their attenuation in towed marine seismic: A tutorial

Geophysics ◽  
2020 ◽  
pp. 1-104
Author(s):  
Volodya Hlebnikov ◽  
Thomas Elboth ◽  
Vetle Vinje ◽  
Leiv-J. Gelius
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Noise Component ◽  
Industry Standard ◽  
Noise Type ◽  
Wide Range ◽  
Different Types ◽  
Seismic Records ◽  
Marine Seismic ◽  
Processing Techniques

The presence of noise in towed marine seismic data is a long-standing problem. The various types of noise present in marine seismic records are never truly random. Instead, seismic noise is more complex and often challenging to attenuate in seismic data processing. Therefore, we examine a wide range of real data examples contaminated by different types of noise including swell noise, seismic interference noise, strumming noise, passing vessel noise, vertical particle velocity noise, streamer hit and fishing gear noise, snapping shrimp noise, spike-like noise, cross-feed noise and streamer mounted devices noise. The noise examples investigated focus only on data acquired with analogue group-forming. Each noise type is classified based on its origin, coherency and frequency content. We then demonstrate how the noise component can be effectively attenuated through industry standard seismic processing techniques. In this tutorial, we avoid presenting the finest details of either the physics of the different types of noise themselves or the noise attenuation algorithms applied. Rather, we focus on presenting the noise problems themselves and show how well the community is able to address such noise. Our aim is that based on the provided insights, the geophysical community will be able to gain an appreciation of some of the most common types of noise encountered in marine towed seismic, in the hope to inspire more researchers to focus their attention on noise problems with greater potential industry impact.

Rolling Element Bearings Fault Identification in Rotating Machines, Existing Methods of Vibration Signal Processing Techniques and Practical Considerations

2013 ◽  
Vol 430 ◽  
pp. 70-77
Author(s):  
Ninoslav Zuber ◽  
Dragan Cvetkovic
Keyword(s):  
Real Life ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Universal Method ◽  
Bearing Failures ◽  
Rolling Element ◽  
Different Types ◽  
Processing Techniques ◽  
Failure Monitoring ◽  
Signal Processing Techniques

This paper addresses the suitability of vibration monitoring and analysis techniques to detect different types of defects in roller element bearings. Processing techniques are demonstrated on signals acquired from the test rig with defective bearings. As a result it is shown that there is no reliable universal method for bearing failure monitoring from its early occurence up to bearings failure. Two real life case studies with different types of bearing failures are presented with practical considerations on methods used for failure identification.

THE MATCHED FIELD PROCESSING BENCHMARK PROBLEMS

1994 ◽  
Vol 02 (03) ◽  
pp. 161-185 ◽  
Author(s):  
MICHAEL B. PORTER ◽  
A. TOLSTOY
Keyword(s):  
State Of The Art ◽  
Lessons Learned ◽  
Benchmark Problems ◽  
Naval Research ◽  
Test Problems ◽  
Acoustic Source ◽  
Matched Field Processing ◽  
True Source ◽  
Processing Techniques ◽  
Signal Processing Techniques

In matched field processing sophisticated acoustic models are combined with signal processing techniques to localize an acoustic source in the ocean. A key challenge has been to develop schemes that work not just in idealized simulations but in realistic scenarios. Additionally it has been difficult to get a sense of the relative merits of different schemes: there has been no common set of problems to test the techniques. To assess the state of the art, a workshop was held in May 1993 at the Naval Research Laboratory where both simulated and experimental data were provided to the community of users to test the algorithms. However, researchers were not given the true source location and thus exercised their algorithms blindly. We describe here the test problems and provide an overview of the results and the lessons learned.

scholarly journals Advanced Multifocus Image Fusion algorithm using FPDCT with Modified PCA

2019 ◽  
Vol 9 (2) ◽  
pp. 175-184
Keyword(s):  
Image Fusion ◽  
Wavelet Transforms ◽  
Quality Evaluation ◽  
State Of The Art ◽  
Principal Component ◽  
Fusion Algorithm ◽  
Pixel Intensity ◽  
Wide Range ◽  
Value Decomposition ◽  
Processing Techniques

Image fusion has been performed and reported in this paper for multi-focused images using Frequency Partition Discrete Cosine Transform (FP-DCT) with Modified Principal component analysis (MPCA) technique. The image fusion with decomposition at fixed levels may be treated as a very critical rule in the earlier image processing techniques. The frequency partitioning approach was used in this study to select the decomposition levels based on the pixel intensity and clarity. This paper also presents the modified PCA technique which provides dimensionality reduction. The wide range of quality evaluation metrics was computed to compare the fusion performance on the five images. Different techniques such as PCA, wavelet transforms with PCA, Multiresolution Singular Value Decomposition (MSVD) with PCA, Multiresolution DCT (MRDCT) with PCA, Frequency partitioning DCT (FP-DCT) with PCA were computed for comparison with the proposed FP-DCT Modified PCA (MPCA) technique. Images obtained after fusion process obtained by the method proposed shows enhanced visual quality, negligible information loss and discontinuities in the image than compared to other state of the art methods.

scholarly journals Signal Processing Techniques for Removal of Various Artifacts from Obstructive Sleep Apnea Signals

2019 ◽  
Vol 8 (12) ◽  
pp. 430-435
Keyword(s):  
Signal Processing ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Body Position ◽  
Well Being ◽  
Motion Artifacts ◽  
Obstructive Sleep ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques

Sleep is judgmental to health and well-being. Deficient quality sleep is similar with a wide range of negative outcomes varies from schizophrenia to cardiovascular disorders. Obstructive sleep apnea is one of the sleep disorders. In order to identify the various syndromes the signals are need to record by using the sensors. Sleep signals are recorded by using the polysomnography (PSG) labs which is the old traditional and gold standard for recording the sleep signals. PhysioNet is a large online medical database that consists of a large collection of recordings of various physiological signals. PhysioNet database consist of sleep apnea database available. Physionet website is a universal service, physionet resource supported by the national institute of health’s National Institute of Biomedical Imaging and Bioengineering (NIBIB) and National Institute of General Medical Sciences (NIGMS). This survey paper aims to bring the different Signal Processing Techniques for Removal of Various Artifacts from Obstructive Sleep Apnea Signals to identify sleep apnea syndrome, because pre-processing is most effective and efficient to reduce unwanted signals from the original sleep signals. While recording the sleep apnea signals various artifacts records along with raw signals either directly or indirectly due to the internal and external sources like Power line interference, Muscle contractions, Electrode contact noise, Motion Artifacts, Baseline wandering, Noise generated by electronic circuits, while breathing and coughing, body position movements etc, and they need to be eliminated in order to acquire genuine health information. So in order to remove there artificats from the sleep signals the signal processing techniques (filtering techniques) are predominantly used for pre-processing of the sleep signals and have been executed in a wide variety of systems for analysis. Filtering of the sleep signal is contingent and should be implemented only when the required statistics remains cryptic

scholarly journals Accelerometer Based Method for Tire Load and Slip Angle Estimation

Vibration ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 174-186 ◽  
Author(s):  
Kanwar Bharat Singh ◽  
Saied Taheri
Keyword(s):  
Signal Processing ◽  
State Of The Art ◽  
Experimental Results ◽  
Slip Angle ◽  
Special Focus ◽  
Promising Technology ◽  
Research Activities ◽  
Novel Algorithms ◽  
Processing Techniques ◽  
Signal Processing Techniques

Tire mounted sensors are emerging as a promising technology, capable of providing information about important tire states. This paper presents a survey of the state-of-the-art in the field of smart tire technology, with a special focus on the different signal processing techniques proposed by researchers to estimate the tire load and slip angle using tire mounted accelerometers. Next, details about the research activities undertaken as part of this study to develop a smart tire are presented. Finally, novel algorithms for estimating the tire load and slip angle are presented. Experimental results demonstrate the effectiveness of the proposed algorithms.

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