We present an overview of the data collection and transcription efforts for the COnversational Speech In Noisy Environments (CO-SINE) corpus. The corpus is a set of multi-party conversations recorded in real world environments with background noise that can be used to train noise-robust speech recognition systems. We explain the motivation for creating such a corpus and describe the resulting audio recordings and transcriptions that comprise the corpus. These recordings include a 4-channel array and close-talking, far-field, and throat microphones on separate synchronized channels, allowing for unique algorithm research.
We implement and evaluate a method infer position from Doppler measurements in a multistatic sonar scenario and present a likelihood approach for doing so. Doppler measurements are used to create likelihood surfaces for each of the transmitter-receiver pairs. The likelihood surfaces are combined and can then be used as-is or combined with additional position measurements. The final likelihood surface is usable in a Bayesian-style tracker or can be used to estimate position of a contact for use in a contact-based tracker. We show how the estimate improves with the addition of multiple receivers and show how the use of Doppler information can improve tracking results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.