6533b858fe1ef96bd12b613e

RESEARCH PRODUCT

Analysis of data fusion techniques for multi-microphone audio event detection in adverse environments

Irene Martin-moratoMaximo CobosFrancesc J. Ferri

subject

Noise measurementEvent (computing)MicrophoneComputer scienceReal-time computingFeature extractionContext (language use)02 engineering and technologycomputer.software_genreSensor fusion030507 speech-language pathology & audiology03 medical and health sciences0202 electrical engineering electronic engineering information engineeringData analysis020201 artificial intelligence & image processing0305 other medical sciencecomputerData integration

description

Acoustic event detection (AED) is currently a very active research area with multiple applications in the development of smart acoustic spaces. In this context, the advances brought by Internet of Things (IoT) platforms where multiple distributed microphones are available have also contributed to this interest. In such scenarios, the use of data fusion techniques merging information from several sensors becomes an important aspect in the design of multi-microphone AED systems. In this paper, we present a preliminary analysis of several data-fusion techniques aimed at improving the recognition accuracy of an AED system by taking advantage of the diversity provided by multiple microphones in adverse acoustic conditions. The results confirm that, under appropriate processing schemes, the recognition rate can be increased as well as the corresponding independence on event location.

yearjournalcountryeditionlanguage
2017-10-012017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)
https://doi.org/10.1109/mmsp.2017.8122274