Musimap: Personalizing Your Music with Emotional AI


Today, music and technology have built an inseparable relationship, influencing the way music is created and how the public consumes it. An example of it is musicians can use a retro video game console to create chiptunes. Music streaming platforms and other apps have also influenced how we see music today.

However, there is still a gap when it comes to classifying music according to music tastes, existing playlists based on personality aspects, and emotions; it sounds ambitious, right? Well, that is exactly what Musimap aims to do.

Musimap makes use of emotional Artificial Intelligence by leveraging 20 years of manual tagging efforts. They provide automated engines to achieve high performances in terms of recommendation, emotional profiling, and metadata enrichment. The company uses proprietary AI to generate emotional metadata for clients. This can be used to search for similar songs, automatically create playlists based on certain genres or moods, and match playlists with personality profiles.

How it all began

Back in 1986 at the University of Louvain in Belgium, several music experts like musicians, musicologists, sociologists, psychologists, and philosophers, got together to create the Musimap’s technology. This group of experts mapped the global music library as a detailed map of the history of music, which then evolved into a self-learning, context-aware neuronal music network.

With other new tech advances, the base algorithm improved to meet current and future technological standards and application requirements. Today, the API system Musimap is available for every related music industry company that wants to empower their company with innovative matching, and profiling capabilities, as well as deepen the connection with their customers.

Musimap analyzes the client’s music catalog to obtain information about genres, sub-styles, vocalness, complex moods, contextual situations, and dozens of other parameters. The APIs are able to scan in just a few seconds using a proprietary database. This matches the data with any client catalog to allow unprecedented accuracy in music recommendation and the creation of advanced music applications.


The Musimap APIs, have various products to give users the best experience they could have, find similar tracks, discover the mood of a track or an entire playlist, and match their personality type on the analyzed track.


MusiMatch will listen to and comprehend music just like humans do. With Musimap, it researches among millions of tracks for particular acoustic patterns, matching your requirements to find the music you need within seconds. Instrumental and vocals are also allowed.

This product uses Machine Learning and Deep Learning technologies to understand the meaning of the content in the music from the audio. More than only simple textual language and labels, it achieves a comparison level just like people would do. While capturing the musically essential information from the audio signal, its state-of-the-art algorithms learn to understand rhythm, beats, styles, genres, subgenre, and even mood in music.

This offering on Musimap aims to help you to search and provide recommendations you need. It can be used for music or video streaming service, digital or linear radio, label, libraries, publishers, in-store music providers, or sync agencies.

Image: María José Acuña Ruiz | TechAcute

Utilizing AI, MusiMotion enriches metadata, tagging tracks with weighted moods, genres, situations, and musical attributes such as key and BPM. It uses a motion-sensitive algorithm that predicts relevant moods, genres, contextual situations, and other key attributes, and assigns weighted relevancy scores.

If you have curation, classification, and recommendation problems, MusiMotion can be a viable option. This option in Musimap analyzes your catalog and tags each track with corresponding metadata, uploads a track, or gives access to a catalog and the engine will the rest.


This product also uses Musimap’s emotion-sensitive algorithm, predicting automatically if a listener is self-aware or spiritual, which activities are they into, and looking beyond the music they like, preferences, and behavior. This option allows companies to get to know and understand customers in a deeper way and provide unique personalized recommendation possibilities.

Like the other two products, MusiMe uses Deep Learning techniques along with a neural network-like database which allows it to analyze a person’s music tastes and listening habits. MusiMe also can extract the psych-emotional portrait of the user, complementing their existing demographic and behavioral statistics to create a complete and evolutive user profile.

This option is great for audio-branding problems. It is meant to be used by digital or linear radio, FMCG/CPG, advertisers, creative agencies, dating companies, in-store music providers,s or in E-commerce.

Prices and featured functions

To acquire the Musimap services, you have several packages you can choose from depending on your needs and budget. You can have the free trial for 21 days which allows you to analyze 100 tracks.

If you want more out of Musimap, they have the Enterprise package with specific offerings, starting from 299€, including Audio Analysis, Storage, and API Access. All the prices changes depending on how many tracks you will analyze and store per month.

Image: María José Acuña Ruiz | TechAcute

For new users, they have the Demos free functions for analyzing tracks. For using the “This Is What You Listen To” option, you just have to enter a Youtube link or upload your own audio file to see what tags MusiMotion is detecting amongst Genres, Moods, Rhythmic Moods, Situations, Acoustic Attributes, and Voice Families.

For the “You Are What You Listen To” option, you will only need to connect with your Spotify account and see what MusiMe is profiling for one of your own playlists amongst MBTI, OCEAN, Ego-Equilibrium, and Enneagram.

Musimap is definitely changing the way music is classified, and how users will consume music. At the same time, they will learn more about tracks, artists, and even about themselves. This fusion of technology, music, and psychology is an interesting tool for companies and music lovers, and it would be interesting to see where it goes in the future.

Photo credits: The feature image has been taken by Elice Moore. All other images shown are taken by the author for TechAcute.

Was this post helpful?

- Advertisment -
- Advertisment -
- Advertisment -
- Advertisment -
- Advertisment -
- Advertisment -