## 4 Applications Utilizing Music Usage Data Based on AI
### Applications Using Music Usage Data
#### 4.2.1 Detecting an Act of Hoarding  
There are various applications involving pseudonymized music usage data. One can expect that such a system could be applied to detect an act of hoarding, because outliers in music usage records could be detected. The so-called act of hoarding refers to an illegal act of manipulating music charts and real-time streaming rankings so that the songs of a specific singer are ranked. Several studies have been conducted to detect fraud and outliers in music streaming data [13; 14]; however, the detection of an act of hoarding with limited music usage data has rarely been researched. An AutoEncoder model can be developed for detecting abnormal music usage records using de-identified music usage data [15]. This model discriminates the noise from the results, rendering the input data and output data equal.  
We conducted an experiment with around 4 million songs' usage data from Genie Music. These data included the number of listeners, the streaming count for each hour, and the accumulated number of listeners and streaming count. We added an index to detect fake streaming, the fake-stream-count, which discriminates fake streaming counts from real streaming. The added index (fake-stream-count) can be calculated using the following equation:  
\[\text{(fake-stream-count)}=\text{(play-in-term/person-max-stream-in-term)}-\text{( listen-in-term)}, \tag{2}\]  
Figure 5: A process diagram for managing the unique key of an independent organization.  
Figure 6: An example of a polymorphic pseudonymization algorithm module.  
where (play-in-term) is the number of total streaming counts, (person-max-stream-in-term) is the maximum number of streaming counts for one person, and (listen-in-term) is the number of listeners per hour. From this index, we developed the proposed AutoEncoder model through the normalization of the index. Figure 7 shows some sample songs taken from among 4 million songs' usage data from Genie Music with several indexes. We tested 3,945,728 data points from December of 2019 to October of 2021. Among the sample data, there were several anomaly points that were dramatically increased, as shown in Figure 8. We took the softmax function as an activation function for minimizing the loss rate. The epochs included approximately 100 tests; the batch size tested was 64-256; and we tested 10, 25, and 50 layers for the final model. Regarding the unsupervised deep learning AutoEncoder model, there was no final goal or objective, so it could be used to analyze several negative values of (fake-stream-counts) as anomaly points, even though these were normal data. According to the results, we could decide if the number of people who streamed the song was smaller than the (listen-in-term) number, meaning that one person listened to a specific song repeatedly [16]. In this case, we could identify one of the candidate songs as subject to an act of hoarding. Before the proper test using the log item standard field data shown in Table 1 was conducted, our preliminary model was tested using a fragmented music usage dataset. We performed the F1-score calculation by using data. The data were randomly chosen from the timepoints of 11:00 am, 8 December 2019 to 12:00 pm, 9 October 2020. During that period, a total of 6236 songs, excluding duplicates, were tested for our proposed model, and 29 songs were derived as anomalies. We set the criteria for F1-score evaluation, and the result was 0.95 [16]. This evaluation could be well derived using the log item standard field. To the best of our knowledge, the proposed method would be the first to be used for music usage data in Korea.