SoundsLikeThis: A music-matching tool for researchers and music-lovers
This tool was created in collaboration with USC's Viterbi School of Engineering with Timothy Greer, PhD.
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From a “seed” song, the tool extracts all of the Spotify MIR features available, then identifies N "matching" songs that align with the user-inputted criteria. The output of this tool is a .csv file that contains the seed song, recommendations, and Spotify MIR features of all songs. The intention of this tool is to allow for greater experimental control for researchers, or to help music listeners find new music that they may enjoy.
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This tool was originally created for a project investigating music-evoked emotion — specifically, nostalgia (Hennessy et al.,, 2024, 2025a, 2025b). We envision that this tool will be useful for many different cases and research questions involving music, psychology, neuroscience, and computer science.
LLM-based automatic scoring of narrative text data
Klus et al., 2025
This tool was created at the University of Arizona, with primary development by Jonas Klus, to automate scoring of text-based autobiographical memory data in accordance with the Autobiographical Interview procedures (Levine et al., 2002). Using Meta's Llama, with LoRA, this tool takes narrative text input and outputs reliable "internal" and "external" detail scores, based on Levine's original scoring procedure. With high alignment with manual scores, this tool effectively reduces processing time and manual labor from months (or often, years) to minutes.
Resources for use are available on OSF.