One of the critical challenges facing 3D video systems and images such as holography lies in their compression technique. High-efficiency video coding (HEVC) has emerged as one of the leading schemes to address this challenge. In this article, a novel method based on wavelet transform is presented to improve HEVC, particularly in digital holography systems (object plane). In this regard, wavelet and resizing are included in the coding process, while extra HEVC decoders and encoders are added to predict and decrease errors in the target. Simulation results reveals that the proposed algorithm reduces Bjøntegaard-Delta (BD) bitrate 17.5% (based on average BD-Rate values) compared to the original HEVC (H.265) scheme while maintaining signal fidelity and even enhancing it slightly. We observe an increased BDpeak-signal-to-noise ratio (BD-PSNR) in real and imaginary parts of digital holograms of high rate quantization values up to 1.1 dB.
Abstract-Speech Recognition Technology can be embedded in various real time applications in order to increase the human-computer interaction. From robotics to health care and aerospace, from interactive voice response systems to mobile telephony and telematics, speech recognition technology have enhanced the humanmachine interaction. Gender recognition is an important component for the application embedding speech recognition as it reduces the computational complexity for the further processing in these applications. The paper involves the extraction of one of the most dominant and most researched up on speech feature, Mel coefficients and its first and second order derivatives. We extracted 13 values for each of these from a data-set 46 speech samples containing the Hindi vowels (आ, इ, ई, उ, ऊ, ऋ, ए, ऎ, ऒ, ऑ) and trained them using a combined model of SVM and neural network classification to determine their gender using stacking. The results obtained showed the accuracy of 93.48% after taking into consideration the first Mel coefficient. The purpose of this study was to extract the correct features and to compare the performance based on first Mel coefficient.
The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.
Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.
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