The goal of the FDAI project is to create a general system that computes an efficient representation of the acoustic environment. More precisely, FDAI has to compute a noise disturbance indicator based on the identification of six categories of sound sources. This paper describes experiments carried out to identify acoustic features and recognition models that were implemented in FDAI. This framework is based on EDS – Extractor Discovery System – an innovative acoustic feature extraction system for sound feature extraction. The design and development of FDAI raised two critical issues. Completeness: it is very difficult to design descriptors that identify every sound source in urban environments, and Consistency: some sound sources are not acoustically consistent. We solved the first issue with a conditional evaluation of a family of acoustic descriptors, rather than the evaluation of a single general-purpose extractor. Indeed, a first hierarchical separation between vehicles (moped, bus, motorcycle and car) and non-vehicles (bird and voice) significantly raised the accuracy of identification of the buses. The second issue turned out to be more complex and is still under study. We give here preliminary results.
https://www.aes.org/e-lib/browse.cfm?elib=13631
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