Predicting Song Popularity Using Neural Networks with Backpropagation Algorithm Based on Audio Features
In the music industry, song popularity is a crucial aspect that significantly impacts the success of artists, producers, and record labels. Understanding how well a song is received can assist stakeholders in making informed decisions about promotional strategies, distribution, and marketing. With the rise of music streaming platforms like Spotify, Apple Music, and YouTube Music, song recommendations and playlists are now tailored based on listener preferences, providing valuable insights into a song's potential popularity.
The prediction of a song's potential popularity has been analyzed by researchers and practitioners through audio data by extracting complex audio characteristics such as tempo, pitch, rhythm, and harmony. One commonly used technique for this purpose is the backpropagation algorithm, which trains neural networks to learn from training data by iteratively updating weights and biases. This results in a model capable of identifying complex patterns within audio features and providing accurate popularity predictions.