Score Formulation and Parametric Synthesis of Musical Track as a Platform for Big Data in Hit Prediction

In today’s entertainment industry which is becoming increasingly competitive, music producers, record labels are striving hard to give the next big hit song and capture the viable music market. We propose to formulate factors and dependency variables which would form the basis of hit prediction in big data environment. The audio features such as pitch and tempo are analyzed in tandem with statistical parameters such as root mean square energy, slope, period frequency, and musical topographies like acousticness, loudness, and instrumentalness. This is a preliminary experiment where the simulated ratings are paralleled with ground truth obtained from Billboard, Spotify, and Radio Mirchi rankings over a period of 5–10 weeks. The paper covers a wide area of tracks from USA, UK, Australia, and India, and proposes to arrive at a consensus to the factors contributing to the success of the track according to their topography. While acousticness plays a vital role in US and India countdowns, British are highly influenced by the danceability and the energy components of the track. The paper provides a cushion for hit prediction classification of musical tracks in big data applications.