Song Recommendation Application Using Speech Emotion Recognition

Abstract

This research was conducted to facilitate the interaction between radio broadcasters and radio listeners during the song request process. This research was triggered by the difficulty of the broadcasters in monitoring song requests from listeners. The system is made to accommodate all song requests by listeners. The application produced in this study uses speech emotion recognition technology based on a person's mood obtained from the spoken words. This technology can change the voice into one of the mood categories: neutral, angry, sad, and afraid. The k-Nearest Neighbor method is used to get recommendations for recommended song titles by looking for the closeness of the value between the listener's mood and the availability of song playlists. kNN is used because this method is suitable for user-based collaborative problems. kNN will recommend three songs which then be offered to listeners by broadcasters. Based on tests conducted to the broadcasters and radio listeners, this study has produced a song request application by recommending song titles according to the listener's mood, the text message, the searching songs, and the song requests and the song details that have been requested. Functional test that has been carried out has received 100 because all test components have succeeded as expected.

Implementation of JIProlog on an Android-based Song Expert System to Provide Song Recommendations Based on 16 Human Personality Types

“…In determining song recommendations, according to some previous references (Ji et al, 2015;Sánchez-Moreno et al, 2020;Kindra et al, 2021;Setiawan, 2021), the system needs to know in advance the facts of the user. Starting from knowing the personality of the MBTI test, which consists of 4 dimensions following some previous references (Dargis et al, 2015;Bess & Harvey, 2002).…”