This research presents a music recommendation system based on multiple users' communication excitement and
productivity. Evaluation is conducted on following two points. 1, Does songA recommended by the system improve the
situation of dropped down communication excitement? 2, Does songB recommended by the system improve the situation
of dropped down and productivity of collaborative work?
The objective of this system is to recommend songs which shall improve the situation of dropped down communication
excitement and productivity. Songs are characterized according to three aspects; familiarity, relaxing and BPM(Beat Per
Minutes). Communication excitement is calculated from speech data obtained by an audio sensor. Productivity of
collaborative brainstorming is manually calculated by the number of time-series key words during mind mapping.
First experiment was music impression experiment to 118 students. Based on 1, average points of familiarity, relaxing
and BPM 2, cronbach alpha factor, songA(high familiarity, high relaxing and high BPM song) and songB(high
familiarity, high relaxing and low BPM) are selected.
Exploratory experiment defined dropped down communication excitement and dropped down and productivity of
collaborative work. Final experiment was conducted to 32 first meeting students divided into 8 groups. First 4 groups
had mind mapping 1 while listening to songA, then had mind mapping 2 while listening songB. Following 4 groups had
mind mapping 1 while listening to songB, then had mind mapping 2 while listening songA. Fianl experiment shows two
results. Firstly, ratio of communication excitement between music listening section and whole brain storming is 1.27.
Secondly, this system increases 69% of average productivity.