Emotion-Aware Music Recommendation

People often listen to songs that match their mood. Thus, an AI music recommendation system that is aware of the user’s emotions is likely to provide a superior user experience to one that is unaware. In this paper, we present an emotion-aware music recommendation system. Multiple models are discussed and evaluated for affect identification from a live image of the user. We propose two models: DRViT, which applies dynamic routing to vision transformers, and InvNet50, which uses involution. All considered models are trained and evaluated on the AffectNet dataset. Each model outputs the user’s estimated valence and arousal under the circumplex model of affect. These values are compared to the valence and arousal values for songs in a Spotify dataset, and the top-five closest-matching songs are presented to the user. Experimental results of the models and user testing are presented

Publicizing the Chamber Music Festival

This project explores the rhetorical relationship between audience and musical taste, and how this impacts the projection of an identity and the design process of chamber music festivals. In order to address the concerns of current audience and the graying process regarding a noticed demographic of current audience members, as well as the pursuit of new audience members, a design portfolio has been created in the form of a redesigned campaign for a local music festival which offers a more effective design and identity-projection solution

NONLINEAR APPROACH IN CLASSIFICATION VISUALIZATION AND EVALUATION

In this paper we have proposed the novel methodology to visualize classification scheme in informatics domain. We have mapped a documents collection of ACM (Association for Computing Machinery) Digital Library to a sphere surface. Two main stages of visualization processes complement one another: classification and clusterization. Primarily classified documents were visualized and their further clusterization by means of keywords was crucial in evaluation process. For clusters analysis of given visualization maps nonlinear digital filtering techniques were applied. The clusters of keywords were characterized by a local accuracy. Obtained semantic map was included to validation process

Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges

Based on the Self-Organizing Map (SOM) algorithm, development of effective solutions for visual analysis and retrieval in complex data is possible. Example application domains include retrieval in multimedia data bases, and analysis in financial, text, and general high-dimensional data sets. While early work defined basic concepts for data representation and visual mappings for SOM-based analysis, recent work contributed advanced visual representations of the output of the SOM algorithm, and explored innovative application concepts

Enhancing Mindfulness-Based Stress Reduction Through a Mobile Application

The goal of this Major Qualifying Project (MQP) was to improve upon the initial design of a mobile application that would assist college students in the practice of Mindfulness Based Stress Reduction (MBSR). The team assisted in building a foundation for UMass Medical so they can apply for a grant to have this project be fully funded and become a fully functioning mobile application. This MQP was a continuation of a previous MQP’s work that explored some of the initial ideas on how to design the application. The team created functional mockups of the original idea and surveyed college students to get their feedback on the initial design