This tool utilizes Spotify's API to find song recommendations for a given, user-inputted "seed" song. From the "seed" song, the tool extracts all of the Spotify MIR features available. Then, the user selects how they would like their output songs to be matched, and the tool identifies N 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.

This tool was originally created for a project investigating music-evoked emotion — specifically, nostalgia. We aimed to use personalized nostalgic stimuli for each participant, but needed to identify a method of finding musically-matched "control" songs to go along with each personalized nostalgic song. The goal of musically matching songs was to disentangle the effects of the music-evoked emotion from the effects of lower-level musical features such as tempo, or loudness. We used this tool to identify a set of 10 candidate non-nostalgic songs that we then presented to participants to identify whether they were familiar, or nostalgic. Familiar, non-nostalgic songs were selected as eventual Control songs for the study.

While the creation of this tool was for one particular case, we envision that this tool will be useful for many different cases and research questions involving music, psychology, neuroscience, and computer science.

1. Collect your seed song(s). In original uses of this tool, we collected seed songs from participants, asking them to list songs that made them feel nostalgic.

2. Decide whether you want to import songs manually or via a spreadsheet.
   a. If manually, type the title and artist of the song into the text box.
   b. If via spreadsheet, download the template spreadsheet and format your list of songs accordingly. Then upload.

3. Once your songs load, select the musical features that you would like to match based on. For example, perhaps you want to find other songs that were released in the same year as your seed song. Or, maybe you want to find songs that have similar levels of "danceability". You may select as many criteria as you like. In the original use of this tool, we found that our published work matching based on valence, energy, release date, and popularity was sufficient to implicitly match for all other features.

4. Click next, and download your output .csv file. This file will contain your seed songs and their features, and your recommendations and their features.

If you use this tool in your research please cite it and let us know, so that we can include your work in our database.

This tool was created at the University of Southern California's Brain and Creativity Institute (directed by Drs. Antonio and Hanna Damasio), the Brain and Music Lab (directed by Dr. Assal Habibi), and the Signal Analysis and Interpretation Laboratory (directed by Shrikanth Narayanan).