“We learn contextual stuff from time of the day and weekdays versus weekend. And listeners aren’t one-dimensional Pandora learns which situations are likely to bring out your inner punk versus your post-work need to zone out and relax.
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Pandora measures engagement based on which songs users skip, either in full or in part, while replays reinforce how much someone likes a specific song even more than a thumbs-up. “It's like ‘Hey, we know barely anything about you,’” says Celma, “so let's give you something that people listened to in this age range or location.” As he puts it, the thumbs-up and thumbs-down functions, which allow users to tell Pandora what they like or what they never want to hear again, “are the strongest explicit signal we have.”īut the implicit feedback is even more telling.
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We have thousands of songs annotated as happy, sad, bittersweet or maybe ‘breakup love songs.’” This kind of data helps Pandora create playlists such as “Sad Country Songs,” compiled by focusing on country tracks that are lower in both BPM and dramatic intensity, may feature a dominant steel guitar and rank at least a 3 on the “melancholy” scale for the lyrics, to get you through a rough patch.Įvery Pandora user begins with “cold-start recommendations,” based on their gender, age and ZIP code. “The mood is a combination of the lyrics and the sonic expression. in mood detection using machine listening,” says Celma. “One staff scientist actually got a Ph.D. To drill down further, lyrics are analyzed for content, tone, mood, even the level of profanity. But a human may be more skilled at picking out the satirical lyrics that distinguish a children’s song from musical comedy meant for adults. It’s difficult for machines to get these attributes - even humans need to pay close attention to understand them.”ĪI may be skilled at distinguishing a prog rock song’s harmonic dissonance, guitar effects and multisectionality from a typical pop song’s simple, diatonic harmonies, synthetic instrumentation and emphasis on the hook. “The nuances that provide the song’s characteristics are the second layer, like subtle melodies or maybe a harpsichord playing. “With a pop song or rock song, you can get the key, the major/minor, the chord progression, the BPM (beats per minute) - all that is not super complex,” Celma says. Now, it’s a constant give and take between the people and the programs, as the musicologists evaluate the accuracy of the machine’s annotations and teach the AI to recognize less apparent attributes. “We use machine listening - machine learning and artificial intelligence applied to music,” says Celma. At first, this process was carried out entirely by human musicologists, who spent 15-30 minutes on each song - but in order for it to scale, technology had to become part of the process.
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