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Figure 4. Hamming Window applied to each frame-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Use of speech spectrum for modifying work domain on signals from time to frequency is made possible using Fourier coefficients. At such applications the rapid and practical way of estimating the spectrum is use of rapid Fourier changes.

Figure 3 Frame blocking of the speech signal-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

The next frame will begin M samples (i.e. 156 samples) after the first frame, and it will overlap the first frame by N-M samples (256 – 156 = 100 samples). Then the third frame will start at 2M samples after the first frame and it will overlap first frame by N-2M. The fourth frame will start at 3M samples after the first, and it will overlap it by N-3M. The process will continue until all input signal is accounted for. The result of this step plotted using MATLAB plot command and displayed in Figure 3. Figure 3 Frame blocking of the speech signal The next step in the processing is to window each individual frame so as to minimize the signal discontinuities at the beginning and end of each frame. The concept here is to minimize the spectral distortion by using the window to taper the signal to zero at the beginning and end of each frame. If we define the window as w(n), 0 ≤ n ≤ N −1, where N is the number of samples in each frame, then the result of windowing is the signal y (n) = x (n)w(n), 0 ≤ n ≤ N −1 l l

opencc-by-4.0Dec 2010View details →

Figure 1. The Structure of the Automatic Translate Voice to Sign Language Animation System-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

All technologies of voice recognition, speaker identification and verification, each has its own advantages and disadvantages and may requires different treatments and techniques. The choice of which technology to use is application-specific. At the highest level, all voice recognition systems contain two main modules: feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each word. Feature matching involves the actual procedure to identify the unknown word by comparing extracted features from his/her voice input with the ones from a set of known words. A wide range of possibilities exist for parametrically representing the speech signal for the voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency Cepstrum Coefficients (MFCC).

Figure 8. Speech signal after frequency wrapping-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Use of speech spectrum for changing the work domain on the signal from time to frequency is used via Fourier conventions. The rapid and practical way of estimating Spectrum in such applications is employment of rapid Fourier transform. The final stage is extracting particularities is use of discrete cosine to return particularities to the time domain and converse FFT approximation. Major advantage of this method is decrease of number of particularities of number of filter from f N to c N in which c f N ≤ N . In addition, doing so, includes making independent of the obtained particularity and rendering them non dependant which leads matrix covariance features to become axial. The following equation shows this point.

Figure 7. Speech signal after FFT-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Figure 2. MFCC Block Diagram Step 1-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Mel Frequency Cepstral Coefficients (MFCC) are coefficients that represent audio, based on perception. It is derived from the Fourier Transform (FFT) or the Discrete Cosine Transform (DCT) of the audio clip. The basic difference between the FFT/DCT and the MFCC is that in the MFCC, the frequency bands are positioned logarithmically (on the Mel scale) which approximates the human auditory system's response more closely than the linearly spaced frequency bands of FFT or DCT. This allows for better processing of data. The main purpose of the MFCC processor is to mimic the behavior of the human ears. Overall the MFCC process has 5 steps that show in figure 2.

Figure 11. A sample of speech to facial animation system-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

To increase the autonomy of deaf and hard of hearing people in their day-to-day professional and social lives, in this paper design and initial implementation of a new approach based on MFCC and Vector Quantization Method is described. This approach includes analyses of speech to animate the talking head. Our future work will include the conception of new test types and performance patterns. We are particularly interested in extending this approach to testing to include implementation of applications under real-time constraints

Figure 5. Mel-spaced filter bank-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Physiologic changes show that human comprehension of frequency content of sound does not obey a linear space. Therefore for each individual it is computed and measure with a real frequency of sound peak at Mel seal. Using the below equation one can change the frequency at Hertz scale to Mel Scale.

opencc-by-4.0May 2024View details →

Dataset for training SENMAP, a automatic tool to curate LTR-retrotransposons using convolutional neural networks

Transposable elements (TEs) are specific structures of the genome of species, which can move from one location to another. For that reason, they can cause mutations or changes that can be negative, such as the appearance of diseases, or beneficial, such as participating in fundamental roles in the evolution of genomes and genetic diversity. Long Terminal Repeat retrotransposons (LTR-RT) are the most abundant in plant species, hence the importance of studying these structures in particular. Over the time, these elements can suffer changes called nested insertions, which can inactivate or modify the functioning of the element, for that they are no longer consider as intact element and cannot be used for identification and classification studies. We create a dataset containing 56,442 LTR-RTs targed as "non-intact" elements and 49,215 considered as "intact".  We formated the sequences IDs in order to keep relevant information as the superfamily and the lineage, as well as the category (Negative for "non-intact" and Positive for "intact" elements).   This dataset (the npy files obtained from the fasta file) was used for training SENMAP, a convolutional neural network architecture to obtain intact LTR-RT sequences in plant genomes, which is composed by four convolutional layers, LeakyReLU as activation function and BinaryFocalLoss as loss function. Achieving an F1-score percentage of 91.37% with test data, identifying low quality sequences rapidly and efficiently, contributing to curate libraries of LTR retrotransposons of plants genomes published in large-scale sequencing projects due to the post-genomic era.

opencc-by-4.0Nov 2020View details →

Data to accompany "Automatic text clustering for audio attribute elicitation experiment responses", AES 143rd Convention, New York, NY, USA, 2017

This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1) and the BBC as part of the BBC Audio Research Partnership. Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00841589. If you use the data, please cite the following paper: J. Francombe, T. Brookes, and R. Mason, “Automatic text clustering for audio attribute elicitation experiment responses”, AES 143rd Convention, New York, NY, USA, 2017

opencc-zeroJun 2024View details →

Dataset: Automatic Data Processing, Inc. (ADP) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: Automatic Data Processing, Inc. (ADP) Stock Performance

Figure 2 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 2. – Three images of organisms obtained by cropping images of lots; from left to right: Chalinidae (Porifera), Polyclinidae (Chordata), Hormatidae (Cnidaria).

Figure 3 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 3. – Image of a batch of macro-invertebrate bycatch organisms from Kerguelen Exclusive Economical Zone (Poker 4 survey, 2017), including corals, a crinoïd, an ophiurid, a sea urchin and a brachiopoda; organisms are incomplete and have been quickly spread out over a small plate to take the picture.

Figure 6 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 6. – Example of detection and classification obtained with an image including an Ophiuroid, a piece of coral and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.

Figure 5 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 5. – Example of detection and classification obtained with an image including Ascidians and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.

Figure 2. – Otolith images from a in Automatic method to transform routine otolith images for a standardized otolith database using R

Figure 2. – Otolith images from a binocular dissecting microscope under different types of illumination: A. Reflected light and B. Transmitted light.

Figure 1 in Automatic method to transform routine otolith images for a standardized otolith database using R

Figure 1. – Different types of otolith images showing common issues. Scale is different between images depending on the sample, some broken otoliths, various exposures and colors, some particles may be present (hair, bubbles).

opencc-by-4.0Aug 2022View details →

CVEfixes Dataset: Automatically Collected Vulnerabilities and Their Fixes from Open-Source Software

CVEfixes is a comprehensive vulnerability dataset that is automatically collected and curated from Common Vulnerabilities and Exposures (CVE) records in the public <a href="https://nvd.nist.gov/">U.S. National Vulnerability Database (NVD)</a>. The goal is to support data-driven security research based on source code and source code metrics related to fixes for CVEs in the NVD by providing detailed information at different interlinked levels of abstraction, such as the commit-, file-, and method level, as well as the repository- and CVE level. This release, v1.0.8, covers all published CVEs up to 23 July 2024. All open-source projects that were reported in CVE records in the NVD in this time frame _and_ had publicly available git repositories were fetched and considered for the construction of this vulnerability dataset. The dataset is organized as a relational database and covers 12107 vulnerability fixing commits in 4249 open source projects for a total of 11873 CVEs in 272 different Common Weakness Enumeration (CWE) types. The dataset includes the source code before and after changing 51342 files and 138974 functions. The collection took 48 hours with 4 workers (AMD EPYC Genoa-X 9684X). This repository includes the SQL dump of the dataset, as well as the JSON for the CVEs and XML of the CWEs at the time of collection. The complete process has been documented in the paper "CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software", which is published in the Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). You will find a copy of the paper in the Doc folder.  Citation and Zenodo links Please cite this work by referring to the published paper: <ul> <li>Guru Bhandari, Amara Naseer, and Leon Moonen. 2021. CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). ACM, 10 pages. <a href="https://doi.org/10.1145/3475960.3475985">https://doi.org/10.1145/3475960.3475985</a></li> </ul> <pre><code>@inproceedings{bhandari2021:cvefixes, title = {{CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software}}, booktitle = {{Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21)}}, author = {Bhandari, Guru and Naseer, Amara and Moonen, Leon}, year = {2021}, pages = {10}, publisher = {{ACM}}, doi = {10.1145/3475960.3475985}, copyright = {Open Access}, isbn = {978-1-4503-8680-7}, language = {en} }</code></pre> The dataset has been released on Zenodo with DOI:<a href="https://doi.org/10.5281/zenodo.4476563">10.5281/zenodo.4476563</a>. The GitHub repository containing the code to automatically collect the dataset can be found at <a href="https://github.com/secureIT-project/CVEfixes">https://github.com/secureIT-project/CVEfixes</a>, released with DOI:<a href="https://doi.org/10.5281/zenodo.5111494">10.5281/zenodo.5111494</a>.