Name
Abstract | ||
|---|---|---|
Auditory contexts are recognized from mixtures of sounds from mobile users’ everyday environments. We describe our implementation of auditory context recognition for mobile devices. In our system we use a set of support vector machine classifiers to implement the recognizer. Moreover, static and runtime resource consumption of the system are measured and reported. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/MDM.2009.74 | Mobile Data Management |
Keywords | Field | DocType |
mobile user,everyday environment,support vector machine classifier,mobile device,auditory context recognition,mobile devices,runtime resource consumption,auditory context,pattern recognition,mobile computing,kernel,accuracy,voting,mel frequency cepstral coefficient,support vector machine,hidden markov models,classification,pervasive computing,support vector machines,feature extraction,layout | Mobile computing,Mel-frequency cepstrum,Computer science,Human–computer interaction,Artificial intelligence,Ubiquitous computing,Distributed computing,Kernel (linear algebra),Support vector machine,Feature extraction,Mobile device,Hidden Markov model,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikko Perttunen | 1 | 61 | 5.81 |
| Max Van Kleek | 2 | 542 | 58.95 |
| Ora Lassila | 3 | 833 | 112.05 |
| Jukka Riekki | 4 | 701 | 85.55 |