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4 results for “animal language”
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
<p>All technologies of voice recognition, speaker identification and verification, each has its<br> own advantages and disadvantages and may requires different treatments and techniques. The<br> choice of which technology to use is application-specific. At the highest level, all voice recognition<br> systems contain two main modules: feature extraction and feature matching. Feature extraction is<br> the process that extracts a small amount of data from the voice signal that can later be used to<br> represent each word. Feature matching involves the actual procedure to identify the unknown word<br> by comparing extracted features from his/her voice input with the ones from a set of known words.<br> A wide range of possibilities exist for parametrically representing the speech signal for the<br> voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency<br> Cepstrum Coefficients (MFCC).</p>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
A Computer Graphics Approach to Creating New Method for Generating 3D Gesture Animations in Bangla Sign Language via HamNoSys to SiGML Conversion
<p>To prepare the system, we employed 94 classes of data. In this dataset, there are 13 Bangla numerical data classes, 36 Bangla alphabet data classes, and 41 Bangla word data classes. Every class of data contains different data types like Hand Shape, Hand Orientation, Hand Movement, Notations, etc. These data were created in SiGML tags. Every class of data is unique and different from others. The system was prepared using BdSL. And BdSL is an uncommon and unique sign language, among others. That's why every class of data is unique and created by us. We search HamNoSys notations for Bangla alphabets, words, and numbers in English HamNoSys datasets (almost 6,000 data). However, we find only 20% of the data, which is quite similar to BdSL. We create 80% HamNoSys notation for BdSL and we modify the matching 20% notations. Then, we converted them into SiGML and made the data classes. This was a big challenge in our research. </p>
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