: We humans are capable of remembering, recognizing, and acting upon hundreds of thousands of different types of acoustic events on a day-to-day basis. Decades of research on acoustic sensing have led to the creation of systems that now understand speech (e.g. a personal assistant like iPhone’s Siri, or the voice activated search feature from Google), recognizes the speaker, and finds a song (e.g., Shazam). However, apart from speech, music, and some application specific sounds, the problem of recognizing varieties of general-purpose sounds that a mobile device encounters all the time has remained unsolved. The goal of this research is to build a platform that automatically creates classifiers that recognize general-purpose acoustic events on mobile devices. As these classifiers are meant to run on mobile devices, the technical goals include energy-efficiency, meeting timing constraints, and leveraging the user contexts such as the location and position of the mobile device in order to improve the classification accuracy.
With this goal in mind, we have built a general-purpose, energy-efﬁcient, and context-aware acoustic event detection platform for mobile devices called – ‘Auditeur. Auditeur enables mobile application developers to have their app register for and get notiﬁed on a wide variety of acoustic events. Auditeur is backed by a cloud service to store crowd-contributed sound clips and to generate an energy-efﬁcient and context-aware classiﬁcation plan for the mobile device. When an acoustic event type has been registered, the mobile device instantiates the necessary acoustic processing modules and wires them together to dynamically form an acoustic processing pipeline in accordance to the classification plan. The mobile device then captures, processes, and classiﬁes acoustic events locally and efﬁciently. Our analysis on user-contributed empirical data shows that Auditeur’s energy-aware acoustic feature selection algorithm is capable of increasing the device-lifetime by 33.4%, sacriﬁcing less than 2% of the maximum achievable accuracy. We implement seven apps with Auditeur, and deploy them in real-world scenarios to demonstrate that Auditeur is versatile, 11.04% − 441.42% less power hungry, and 10.71% − 13.86% more accurate in detecting acoustic events, compared to state-of-the-art techniques. We perform a user study involving 15 participants to demonstrate that even a novice programmer can implement the core logic of an interesting app with Auditeur in less than 30 minutes, using only 15 – 20 lines of Java code.