Audio Filters for Music Styles

With today’s digital audio workstations, novice audio engineers may find themselves overwhelmed by the number of options when applying audio effects. Experienced engineers, by contrast, purposefully apply large numbers of audio effects to achieve intended sonic outcomes. The objective was to review the effect chains used by professionals and create a technology-based resource to help novice engineers apply and manage sets of effects over multiple tracks. We created a prototype that provides users a drop-down menu with a set of preset effects associated with those music genres. We surveyed a group of novice audio engineers about their experience using the prototype with a an audio project that initially had no effects. Results showed a generally positive reception to using such a prototype

Sensing Depression

The hallmark indicator of depressive disorders is a presence of sad, empty, or irritable mood, accompanied by somatic and cognitive changes that significantly affect the individuals capacity to function. The overall goal of our project is to provide a tool for doctors to effortlessly detect depression, and in effect achieve greater coverage in detecting depression over the general population. We use machine learning techniques to create a mobile application that infers a smartphone users severity of depression from data scraped off their phone and social media websites. Through our study, we have demonstrated the feasibility of this approach to diagnosing depression, achieving an average testset RMSE of 5.67 across all modalities in the task of PHQ-9 score predictions