1/20/2024 0 Comments Npr echoes playlist tonight![]() ![]() For example, we can make the following call to see if Led Zeppelin’s “Going to California” is acoustic or electric: You can retrieve the song_type for any song by adding the 'song_type’ bucket as a parameter to any API call that returns song information. Likewise, we’ve labeled all music that we are confident in being electric with the 'electric’ song type. We’ve gone through the millions of songs in our database of music and, using the new acousticness score for each song, have labeled songs that we are very confident as being acoustic with the 'acoustic’ song type. If you don’t want to be bothered with fine tuning the acousticness threshold, you can take advantage of our new 'electric’ and 'acoustic’ song types. You can filter out these tracks simply by setting a max_speechiness threshold of. That’s because the spoken human voice is quite acoustic. In particular if you are looking for acoustic songs by setting a high min_acousticness threshold you may notice that you get spoken word tracks such as artist interviews and commentary. Of course you can combine the acousticness parameter with other parameters as well. For example the best threshold to find acoustic dubstep (of which there is very little) may be different than the best threshold to find acoustic folk (of which there is quite a lot). Picking the best acousticness threshold to guarantee that most songs in a playlist will be all acoustic can be a bit tricky. This will help us quickly converge on the ideal acousticness model. ![]() We are continually expanding our model so when we find outliers that are mislabelled, we add those tracks to our training set. However, we don’t always get it quite right. We think we’ve done a really good job of building a model that can accurately predict the acousticness or electricness of a song. Some Important Caveats: This is a beta release of acousticnesss. For example here’s a playlist that slowly transitions from mostly acoustic to mostly electric Beatles songs: You can even use acousticness as a parameter for sorting allowing you to easily create a playlist that gradually segues from acoustic to electric or vice versa. ![]() Similarly if you want music from Bob Dylan’s electric era you can make a call like so: For example, you can create a playlist of folk Bob Dylan music by constraining the acousticness to be above a certain value like so: You can also use acousticness as a filter when you are creating playlists. Indicating that the track is not very acoustic at all. Compare that to a song like Helter Skelter by The Beatles with a call like so: Īnd you’ll see a very low acousticness value like so: "acousticness": 0.03987356767279777, Which indicates that the song is quite acoustic. The resulting data returned includes an 'acousticness’ attribute in the audio_summary data with a value: "acousticness": 0.8857553650609994, For instance, to see how acoustic the song 'Yesterday’ by the The Beatles is, you can make a call like: To get the 'acousticness’ for any song you just add the 'audio_summary’ bucket to any Echo Nest API call that returns song information. This means we can easily apply this model to our many millions of songs allowing us to get an estimate of the acousticness for just about any song that has ever been recorded. We’ve built and trained machine models that will predict the acousticness based upon the audio signal. We derive this new parameter directly from audio. Songs with high 'acousticness’ will consist mostly of natural acoustic sounds (think acoustic guitar, piano, orchestra, the unprocessed human voice), while songs with a low 'acousticness’ will consists of mostly electric sounds (think electric guitars, synthesizers, drum machines, auto-tuned vocals and so on). ![]() The attribute, called ‘acousticness’, is an estimate of how acoustic a particular song is. This week we are adding a new audio attribute to our set of data on millions of songs. New Audio Attribute: Acousticness June 21, 2013 ![]()
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