2022-11-20, 13:30–14:00 (Europe/Athens), Room III
An annotation tool helps people (without the need for specific knowledge) to mark a segment of an audio file (waveform), an image or text etc. in order to specify the segment’s properties. Annotation tools are used in machine learning applications such as Natural Language Processing (NLP) and Object Detection in order to train machines to identify objects or text. While there is a variety of annotation tools, most of them lack the multi-user feature (multiple users annotating a single project simultaneously) whose implementation is planned in this project. The audio annotation process is usually tedious and time consuming therefore, these tools (annotation tools which provide the multi-user feature) are necessary in order to reduce the effort needed as well as to enhance the quality of annotations. Since in most tasks related to audio classification, speech recognition, music detection etc., machine and deep learning models are trained and evaluated with the use audio that has previously been annotated by humans, the implementation of such a tool will lead to higher accuracy of annotated files, as they will have been annotated by more than one human, providing a more reliable dataset. In effect, multi-user annotation will reduce the possibility of human error e.g. an occasional mistaken labelling of a segment might be pointed out by another annotator. Deep learning models can be used for annotation and can kickstart your development effort by enabling faster annotation of datasets for AI algorithms. Deep learning models are sensitive to the data used to train them, this makes it hard to train the deep learning models on a specific dataset and deploy them on a different dataset. As a solution, transfer learning for sound could help adapt pretrained models into various datasets. Deep learning models used for annotation can be tuned and improved by retraining these pretrained models based on new datasets.
This project is an enhancement to the previous work that has been done and presented last year in Label Buddy. Its goal is to make annotation simple and easy while also providing a well-defined manager-annotator-reviewer framework. The goal of this project is to use AI approaches to make the annotation process easier for the user by offering label predictions. This way, it will be possible to accomplish more with less data and effort.
Fluent in English (Michigan Proficiency) and Spanish accustomed to working with cross-cultural, global teams. Excellent communicator with ability to meet deadlines and quickly resolve issues. Goal-oriented with strong commitment to collaboration and solutions-oriented problem-solving. Committed to high standards user experience, usability and speed for multiple types of end-users. Bilingual with customer-driven nature and focus on working as part of team. Fan of Hackathons and Coding Competitions.
Hobbies include movies/series, food and gaming and more passionately basketball, music, travelling with friends and making people laugh. I just love to make an impact at everything I do and make my environment happier and better.