The AI Music Generator: How Machine Learning Is Altering How We Experience Music


Music has had a major impact on my life, and I am aware that I am not alone in feeling this way; a large number of people all over the world share my sentiments. This prompted me to design a song generator with artificial intelligence.

Music piques our curiosity in new things, keeps us energized throughout the day, and allows us to briefly escape the stresses of daily life.

Spotify satisfies this demand by offering a staggering number of personalization possibilities. The feature that stands out to me the most is the weekly playlist that they provide.

Every Monday, Spotify provides seventy million users with a fresh playlist personalized to their musical tastes. There are a total of thirty songs on these playlists.

As a fan of electro-indie music, the Discover Weekly playlist is where I 'discovered' some of my favorite tracks. It is safe to believe that Spotify knows more about my music collection than anyone else, but how is that possible?

how is it that spotify is able to accommodate our individual musical preferences?

The answer is an underlying machine learning algorithm that can search the vast world of music to offer us with the music we wish to hear.

The following is a summary of a key algorithm that Spotify has included into its platform to make this functionality possible.

The CNN is comprised of the following components: Occasionally, it may be really tough to comprehend what CNN is all about. How does the procedure operate precisely? Every machine learning model requires some type of input, and in the instance of Spotify's CNN, the input is an array containing information about the frequency, length, and amplitude of a given note at a certain time in a song.

After putting the important elements of the music in a matrix or array, the data from that matrix or array is multiplied by another array known as a filter to determine whether or not a certain feature is present in a song (tone, melody, pitch, mood etc.).

If the total of the heavy bass filter array and song input array is zero, for instance, we know that the music in question was not made by Bassnectar. Following this iterative procedure, a global temporal pooling layer is applied to gather insightful statistics such as the mean and maximum occurrences of a certain feature of a song, such as a chord progression. These statistics can be used to improve the quality of music creation.

This merely contributes to the process of further identifying a song's genre based on its audio signals, and it is then used as input for a fully linked layer. This leads to the construction of a one-dimensional array containing values that correspond directly to the music in question.

Now that our morning 'pump ups,' chill rewinds, and summer jams can all be represented by an array, Spotify can discover thirty additional arrays (songs) with values that are the closest match.

This is what enables Spotify to continue feeding our passion for music and exposing us to sounds we might have never heard otherwise. How does Spotify intend to utilize neural networks and machine learning to enhance the way users interact with music?