MARingBA: Music-Adaptive Ringtones for Blended Audio Notification Delivery

Audio notifications provide users with an efficient way to access information beyond their current focus of attention. Current notification delivery methods, like phone ringtones, are primarily optimized for high noticeability, enhancing situational awareness in some scenarios but causing disruption and annoyance in others. In this work, we build on the observation that music listening is now a commonplace practice and present MARIngBA, a novel approach that blends ringtones into background music to modulate their noticeability. We contribute a design space exploration of music-adaptive manipulation parameters, including beat matching, key matching, and timbre modifications, to tailor ringtones to different songs. Through two studies, we demonstrate that MARIngBA supports content creators in authoring audio notifications that fit low, medium, and high levels of urgency and noticeability. Additionally, end users express a preference for music-adaptive audio notifications over conventional delivery methods, such as volume fading.

More information

Audio samples are available at https://drive.google.com/drive/folders/1o9zjeyXjC5mctqHrWbR-0WtdkMfi1pFd?usp=sharing

@inproceedings {Wang24, 
 author = {Wang, Alexander and Cheng, Yi Fei and Lindlbauer, David}, 
 title = {MARingBA: Music-Adaptive Ringtones for Blended Audio Notification Delivery}, 
 year = {2024}, 
 publisher = {Association for Computing Machinery}, 
 address = {New York, NY, USA}, 
 url = {https://doi.org/10.1145/3613904.3642376}, 
 doi = {10.1145/3544548.3581027}, 
 keywords = {Notifications, Adaptive Interfaces, Music Computing}, 
 location = {Honolulu, HI, USA}, 
 series = {CHI '24} 
 }