This paper presents Daft-Exprt, a multi-speaker acoustic model advancing the state-of-the-art on inter-speaker and inter-text prosody transfer. This improvement is achieved using FiLM conditioning layers, alongside adversarial training that encourages disentanglement between prosodic information and speaker identity. The acoustic model inherits attractive qualities from FastSpeech 2, such as fast inference and local prosody attributes prediction for finer grained control over generation. Experimental results show that Daft-Exprt significantly outperforms strong baselines on prosody transfer tasks, while yielding naturalness comparable to stateof-the-art expressive models. Moreover, results indicate that adversarial training effectively discards speaker identity information from the prosody representation, which ensures Daft-Exprt will consistently generate speech with the desired voice. We publicly release our code 1 and provide speech samples from our experiments 2 .
In this paper is described an experimental passive localization system based on SDR (Software Defined Radio) components. This system is designed to measure Time Differences of Arrival (TDOA) of radar pulses between two platforms. For a TDOA system, time error between the two receivers must be kept very low, which requires a very accurate way to synchronize the time bases. In this purpose, a custom offline synchronization method is proposed. The overall performances of the system are analyzed. In a small scale outdoor experiment, it has been shown to perform TDOA measurements accurately. The performances measured during this experiment are then extrapolated to a more realistic electronic warfare scenario.
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