This paper investigates the problem of predicting popularity of music. In particular, we consider musical complexity as a cue that can be extracted from the audio signal and used for popularity prediction. In addition, we examine the effectiveness of the early stage popularity for long-term popularity prediction. We formulate the popularity prediction problem as a classification problem predicting popularity evolution patterns in a music ranking chart, such as the highest rank of a song over the whole time period, the growth/declination rate in the chart, the duration for which the song appears in the chart, etc. We conduct an experiment with the data collected from the Billboard Rock Songs Chart for about five years. It is found that the two types of features are effective for predicting popularity patterns when used together.
|Title of host publication||SLAM 2015 - Proceedings of the 2015 Workshop on Speech, Language and Audio in Multimedia, co-located with ACM MM 2015|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|Publication status||Published - 2015 Oct 30|
|Event||3rd Workshop on Speech, Language and Audio in Multimedia, SLAM 2015 - Brisbane, Australia|
Duration: 2015 Oct 30 → …
|Name||SLAM 2015 - Proceedings of the 2015 Workshop on Speech, Language and Audio in Multimedia, co-located with ACM MM 2015|
|Other||3rd Workshop on Speech, Language and Audio in Multimedia, SLAM 2015|
|Period||15/10/30 → …|
Bibliographical noteFunding Information:
This research was supported by the MISP (Ministry of Science, ICT and Future Planning), Korea, under the "IT Consilience Creative Program" (IITP-2015-R0346-15-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion).
© 2015 ACM.
All Science Journal Classification (ASJC) codes
- Linguistics and Language
- Speech and Hearing
- Computer Vision and Pattern Recognition