There’s no shortage of articles and papers trying to explain why a song became a hit, and the features hit songs share. In this research we raise the question if it is possible to classify a music track as a hit or a non-hit based on its audio features. The input to each al-gorithm is a series of audio features of a track. This paper takes a stand that music prediction is yet not a data science activity. This gives you a hit-prediction score. Available: 10.1017/s1355771896000222. We then enriched the data using Spotify’s API. The lyric- based features are slightly more useful than the acoustic features in correctly identifying hit songs. Otherwise, it does not count as a hit. early adopter behaviour perform well when predicting top 20 dance hits. According to one music tech startup, its new technology may have. 51, creativity", Organised Sound, vol. In this study, we investigate the relationship between the music listening behaviors of Twitter users and a popular music ranking service by comparing information extracted from tweets with music-related hashtags and the Billboard chart. which what became the normal song length until now. From then on, danceable songs were more likely to become a hit. however, If audio characteristics such as loudness are to be properly evaluated, then it appears that the song would need to be fully written, produced, mixed and mastered before it could be properly assessed — with the consequent expenditure of time, money and effort that entails. The results show that models based on, Music Information Research requires access to real musical content in order to test efficiency and effectiveness of its methods as well as to compare developed methodologies on common data. Four machine learning algorithms (Logistic Regression, Decision Trees, Naïve Bayes and Random Forests) to answer the question-Is there a magical formula for the prediction of hit songs? Check your inboxMedium sent you an email at to complete your subscription. Available: https://www.vice.com/en_us/article/bmvxvm/a, billboard: mining music listening behaviors of twitte. Join ResearchGate to find the people and research you need to help your work. Well, in part it reveals the kinds of sounds that we tend to see more commonly in music: plucky, upbeat majors tend to beat out the moodier minors. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. Increasing the strength of social influence increased We developed two parallel text based and audio based models and further, fused these heterogeneous feature maps taken from intermediate layers to complete the architecture. The model assigns a weight to each song feature, and then uses these weights to predict whether a song falls in the "hit" or "non-hit" category. They marked out the features that, were marked Low, Medium, and High. Machine learning, Supervised learning, ta available and uses other platforms like, of a song to predict success. Study of Inequality and Unpredictability in an Artificial. The accuracy is close to 86% since our model tends to predict that the song is systematically not a hit. We took a closer look at the properties of a song itself and the artists, to see if they might help us in predicting what will be the next hit on the Billboard Top 100. Our experiment uses two audio feature sets, as well as the set of all the manually-entered labels but the popularity ones. Here, we will try to go a bit further and build a hit song classifier. The mean value for duration is 218387 milliseconds, which is approximately 3 minutes and 38 seconds.