Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

High-Accuracy Low-Cost RTK-GPS for an Unmanned Surface Vehicle

This work presents a low cost RTK-GPS system for localization of unmanned surface vehicles. The system is based on the use of standard low cost L1 band receivers and in the RTKlib open source software library. Mission scenarios with multiple robotic vehicles are addressed as the ones envisioned in the ICARUS search and rescue case where the possibility of having a moving RTK base on a large USV and multiple smaller vehicles acting as rovers in a local communication network allows for local relative localization with high quality. The approach is validated in operational conditions with results presented for moving base scenario. The system was implemented in the SWIFT USV with the ROAZ autonomous surface vehicle acting as a moving base. This setup allows for the performing of a missions in a wider range of environments and applications such as precise 3D environment modeling in contained areas and multiple robot operations.