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Register a new userSynchronizing Audio-Visual Film Stimuli in Unity (version 5.5.1f1): Game Engines as a Tool for Research
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Unity is a software specifically designed for the development of video games. However, due to its programming possibilities and the polyvalence of its architecture, it can prove to be a versatile tool for stimuli presentation in research experiments. Nevertheless, it also has some limitations and conditions that need to be taken into account to ensure optimal performance in particular experimental situations. Such is the case if we want to use it in an experimental design that includes the acquisition of biometric signals synchronized with the broadcasting of video and audio in real time. In the present paper, we analyse how Unity (version 5.5.1f1) reacts in one such experimental design that requires the execution of audio-visual material. From the analysis of an experimental procedure in which the video was executed following the standard software specifications, we have detected the following problems desynchronization between the emission of the video and the audio; desynchronization between the temporary counter and the video; a delay in the execution of the screenshot; and depending on the encoding of the video a bad fluency in the video playback, which even though it maintains the total playback time, it causes Unity to freeze frames and proceed to compensate with little temporary jumps in the video. Finally, having detected all the problems, a compensation and verification process is designed to be able to work with audio-visual material in Unity (version 5.5.1f1) in an accurate way. We present a protocol for checks and compensations that allows solving these problems to ensure the execution of robust experiments in terms of reliability.
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Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the IFSpectrogram). Many other synthesis systems use representations derived from the magnitude spectra, and then depend on a backend component to invert the output magnitude spectrograms that generally result in audible artefacts associated with the inversion process. However, for signals that have closely-spaced frequency components such as non-pitched and other noisy sounds, training the GAN on the 2-channel IFSpectrogram representation offers no advantage over the magnitude spectra based representations. In this paper, we propose that training GANs on single-channel magnitude spectra, and using the Phase Gradient Heap Integration (PGHI) inversion algorithm is a better comprehensive approach for audio synthesis modeling of diverse signals that include pitched, non-pitched, and dynamically complex sounds. We show that this method produces higher-quality output for wideband and noisy sounds, such as pops and chirps, compared to using the IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable to using the IFSpectrogram, even while using a simpler representation with half the memory requirements.
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