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| Content Provider | ACM Digital Library |
|---|---|
| Author | Hu, Pan Asare, Philip Dickerson, Robert F. Shen, Guobin Stankovic, John A. Jiang, Xiaofan Hong, Dezhi Nirjon, Shahriar Li, Qiang |
| Abstract | Auditeur is a general-purpose, energy-efficient, and context-aware acoustic event detection platform for smartphones. It enables app 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 user contributed sound clips and to generate an energy-efficient and context-aware classification plan for the phone. When an acoustic event type has been registered, the smartphone instantiates the necessary acoustic processing modules and wires them together to execute the plan. The phone 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 present a user study to demonstrate that novice programmers can implement the core logic of interesting apps with Auditeur in less than 30 minutes, using only 15 - 20 lines of Java code. |
| Starting Page | 403 |
| Ending Page | 416 |
| Page Count | 14 |
| File Format | |
| ISBN | 9781450316729 |
| DOI | 10.1145/2462456.2464446 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-06-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Soundlets Acoustics Auditeur |
| Content Type | Text |
| Resource Type | Article |
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