What is the Music Genome Project?
Inspired by the Human Genome Project of the 1990s and early 2000s, the Music Genome Project (MGP) was conceived by Pandora founder Tim Westergren to catalog the fundamental characteristics of the vast body of recorded music. The goal of this ambitious project was to allow music lovers to discover music based on inherent musical qualities, rather than sales data or industry-backed marketing. The Music Genome Project provides a detailed analysis of millions of songs, describing features of harmony, rhythm, melody, vocals, instrumentation, lyrics, and more. This data powers our best-in-class recommendation algorithms. Thanks to the rigorous and detailed music analysis of the Music Genome Project, none of our competitors can come close to the depth of our content understanding.
Who are the Music Analysts and what do we do?
We are a group of trained musicians, working together to describe the music you listen to on Pandora. Leveraging our expertise in music theory, genre, and music production, we listen to individual songs and tag them with the musical attributes from the Music Genome. Over the past 20 years we have analyzed 2.2 million songs. With a Pandora music catalog in the tens of millions, we rely on a number of methods for prioritizing the most important songs to analyze. Assisting us in this prioritization effort is our amazing Curation team, who are top experts in their respective genre areas. Our data science team then helps us leverage the rich data from the MGP to extrapolate further information about other related material in order to unlock content understanding around more songs, albums, and artists. So how do we analyze a song? Let’s dive in.
When analyzing a song, we often start with the genre, choosing from one or more of the 1300+ subgenres we have developed in our comprehensive genre taxonomy. Since many songs fit into more than one main genre, or borrow influences from a variety of genres, our nuanced analysis process allows us to indicate multiple genres and influences. Training is an essential ingredient for the Music Analyst team. In addition to extensive new hire training, ongoing training is required for maintaining broad genre knowledge, and for staying on top of the latest trends in music.
Next, we apply our music theory backgrounds to assess the musicological features of the song. Is this song in a major or minor key, or somewhere in between? How complex is the harmony? Are there many chord changes, and how often do they repeat? What is the time signature and feel? Does this song have a heavy backbeat, a swing or shuffle feel, or an Afro-Latin beat? How is the melody presented, and how would you describe it musicologically?
Now we put on our producer caps and listen carefully to the arrangement and instrumentation of the song. What instruments are present and what are their roles? Is that trombone taking lyrical solo, and does it have a blaring or a mellow tone? Are those synthetic or acoustic drums? We also listen carefully to the vocalists and describe their timbres and characteristics, which can have an enormous impact on the overall character of the song. Is this singer passionate, or laid-back? Are they singing in a high or low register, and are they gruff and growly or light and breathy?
We also listen to lyrics of the song and broadly assess the main themes and subjects. Is this a tender love song, a bittersweet breakup song, or a brash boasting song? Are the lyrics snarky and cynical, poetic and metaphorical, or laden with vernacular slang? Is this squeaky clean, or are there swear words or potentially offensive themes?
After so much close listening we can step back and assess some broad overall characteristics of the song. Is this recorded live or in the studio? Is the production polished and sparkly, or does it have more of an underground vibe? What is the balance between acoustic, electric, and synthetic sonorities? What dominates the compositional balance of the song, is the emphasis on the lead vocals, the groove, or the performance? Finally, we can step even further back and assess the dominant mood or moods present in the song, including valence and arousal levels, using the newest taxonomy we have developed, called AMT. Before we complete and submit the analysis, we ask ourselves, what is the most salient and notable aspect of this song? If you were to describe this song to a friend, what is the one thing you would call out, and have you done that with this analysis?
As you can see, the level of depth and detail that goes into tagging a single song is unmatched anywhere in the streaming music universe. We have provided this kind of analysis across millions of songs, which we then leverage to further our content knowledge across tens of millions more songs that make up our full catalog. Our data science team has used this massive treasure-trove of data to create the best music recommendation systems in the world, armed with the most comprehensive and complete content understanding database available anywhere. We believe that the next song matters, and that is why we work hard to make our streaming service the best in the world. I hope this helps explain why when that next song comes on your Pandora station, the recommendation we provide is the absolute best available.