## 4 Applications Utilizing Music Usage Data Based on AI
### Applications Using Music Usage Data
#### 4.2.2 Prediction of the Marketability of Music  
Another utilization of music usage data is to predict the marketability of music. Previously, the Korean music industry was limited to a focus on music sales. However, as the music industry is a fairly large market, it could be expanded to other fields. For example, by using music usage data from the past, it may be possible to predict the marketability of specific music or artists, and this could help to expand the scope of the industry through predictions based on data, as in the case of Moneyball in baseball or Opta in soccer.  
Music usage data are time-series numerical data and are non-stationary; thus, we used the ARIMA model for the prediction of the marketability of music. ARIMA means AutoRegressive Integrated Moving Average, which is effective for analyzing time-series data [17]. However, music usage data are non-stationary, so we altered our data to make them appear stationary. The prediction data could be generated using the ARIMA model from the Python library, and the number of listeners per hour, shown in Figure 7, was needed for the prediction. For the proper ARIMA model, we adopted the AIC (Akaike Information Criterion) score, which is a criterion used to evaluate the quality of a statistical model. Using this, we set boundaries of error and improved the accuracy of the prediction  
Figure 7: Sample songs, including usage data with several indexes (several songs and singers were written in Korean).  
in terms of the distance between the prediction data and the actual data. Figure 9 shows the validity of our proposed model during the five months after the release of a specific song. The blue line indicates the number of streaming counts of the song as training data. The green line and red line show the difference between the actual streaming data of the song and its predicted data. We can see a similar tendency after October 2021. As shown in Figure 9, the streaming number of the streamed song shows a sharp increment at the release time and then stabilizes. We concluded that our model is only effective when it is independent of external valuables. For example, when the song was disseminated by the mass media or news, the streaming number could increase. Our model could not reflect this; however, it could be supplemented with an index of SNS data or news crawling data as input data. One can expect that SNS data such as the Google trend value for the specific song could be an index as an active valuable. An evaluation metric such as MAPE could be a candidate for estimating accuracy; however, the precision could vary depending on various conditions. The results could be enhanced through the elaborate adjustment of the index or by combining various indexes.  
Figure 8: An example of an anomaly in the detection of music streaming. The horizontal axis denotes each song, and the \(y\) axis denotes the normalization results of the AutoEncoder model. A red dot indicates anomalous data of the streamed song.  
Figure 9: A prediction graph based on music usage during five months (19 May 2021 to 6 October 2021) after releasing a song. The blue line indicates training data, the green line indicates test data (actual data), and the red line indicates the predicted data after five months.