Saanvi Bhargava's Homepage

About Me

Saanvi Bhargava is a junior at The Harker School in San Jose. Saanvi loves to sing and has been performing since the age of 6. She started learning computer science in middle school and has been recognized for her problem-solving projects in national competitions and in the Synopsis Science Fair. She is the Founder and President of the Beats and Bytes Club, which explores how one can analyze music through technology. Saanvi has been researching how to make music education accessible to more people. Her learnings are published on her blog and all her past projects can be found at her GitHub repo. A talented and trained singer in western classical, she is part of her school’s prestigious show choir group as an alto. In addition to these interests, she is also President-elect for multiple clubs at her school most notably the Future Problem Solving club which focuses on solving practical problems facing our society. She is applying that interest, and her skill in coding and machine learning in exploring how to make music a part of more people’s lives.

Recent Research

A Supervised Learning AI Model for Automated Holistic Vocal Performance Feedback

Scalable music education requires giving fast feedback on student audio performances. Current manual feedback mechanisms are given by teachers, rendering them subjective and, therefore, sometimes inaccurate. Current technological feedback mechanisms evaluate whether a student is correct on a single note rather than the entire music piece, not providing cumulative or numerical feedback. An AI model is presented for automatically grading vocal music recordings cumulatively on pitch and rhythm given a reference piece of music. The model predicts a numerical grade for the performance of a reference piece of music. The ML model is then tested for accuracy on a dataset of corresponding audio recordings (performance and reference) and tagged human scores for these performances. Besides demonstrating the feasibility of developing an objective music grading system, the investigation presented in this paper also reveals some important limitations and subjectivity of current music grading systems, opening opportunities for future work in the community.

Generative Singing Style Transfer Across Genres

Voice style transfer has focused on transforming speech between speakers, leaving singing style, independent of speaker, unexplored. We introduce SingStyleTransfer, a VAE-GAN to perform singing style transfer across genres. The model is evaluated on the SingStyle111 dataset for its ability to carry out genre-to-genre transformations.

The Role of Facial Features and Mannerisms in Detecting Deepfakes

This project seeks to develop a machine learning model to identify deepfakes to prevent the spread of misinformation in this era of technology. Politicians and celebrities are the most affected by deepfakes, since fake videos could endanger their reputation and their careers. Most of the current approaches attempt to create a single model across different videos and using that for detection, which does not yield very accurate results. This study focuses on deepfakes with a single face and attempts to use facial feature extraction for detection of deepfakes. I propose a novel approach of using facial features such as facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation for classification. I conducted 10 different experiments building models for detection using classification algorithms and concluded that 9 of them had an accuracy higher than 95% using the facial feature extraction approach (using OpenFace2). The key finding of this research is that features extracted using the Openface2 library are extremely effective signals for classification of deepfakes involving a single face.

Minds and Machines: Tracing the Evolution of Artificial Neural Networks

As society stands at the crossroads of innovation, tracing the path from the creation of Rosenblatt's Perceptron and Parallel Distributed Processing (PDP) to the ever-evolving landscape of current AI, the journey into the origin of Artificial Intelligence becomes truly riveting. The human fascination with understanding the brain and intelligence has consistently inspired endeavors to emulate these phenomena. In the past, scientists have embarked on a path that aimed to model the workings of the human brain. Consequently, a substantial portion of the following research and achievements in this domain emerged from individuals with expertise in psychology, neuroscience, and computer science. The first major discovery relating to neural networks occurred in the early 1940s, with McCulloch and Pitts.

Cracking Crime with Code: The Applications of AI in Law Enforcement

Crime does not wait for anyone, but what if the solution could anticipate it? The role of neural networks in our everyday lives has grown exponentially since the first computational models of neurons were made in the 1940s. From phones to cars to finances and medical care, Artificial Intelligence shifts the way we live. In recent years, with more interest and advancement in Generative AI, more people have been using large-scale neural networks for daily tasks, such as for learning about topics or for planning schedules. However, on a greater scale than just being an assistant for normal citizens, AI has the potential to revolutionize law enforcement processes and justice proceedings. Through generating sketches, deepfake video detection, noise distinction detection, data analysis advancements, and voice cloning, there can be huge benefits for crime control from AI. However, even with these advantages and advancements, there can be bias in algorithms, and we need to consider ethical considerations of using AI for crime-solving.

From Cash Registers to Clicks: The Sonic Revolution

In the early 1990s, music shops were pulsating with anticipation as fans flocked to bustling record and CD shops to get their hands on a copy of Nirvana’s newest album, Nevermind. The air charged with the excitement of discovering new music and the familiar “ding!” of the cashier ringing up each sale resonated over the excited chatter. The transaction was a straightforward exchange. One new CD for $15.981. Fast forward to today where the music landscape has undergone a revolutionary transition. The era of physical CDs has given way to a digital age where music is accessible with just a couple of clicks for no cost at all. With the introduction of Napster, iTunes, Soundcloud, and other digital streaming services, music has transformed from a private good to a public good, affecting the way artists profit through streams, and increasing artists' reliance on concerts, media coverage, virality, and merchandise.