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Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multi-generator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task.

Audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a spectrogram inversion algorithm to retrieve time-domain signals. In particular, the multiple input spectrogram inversion (MISI) algorithm has been exploited successfully in several recent works. However, this algorithm suffers from two drawbacks, which we address in this paper.

State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user's interest from his listening habits and similarities with other users' profiles. These approaches are agnostic to the song content, and therefore face the cold-start problem: they cannot recommend novel songs without listening history. To tackle this issue, content-aware recommendation incorporates information about the songs that can be used for recommending new items.

Time-frequency audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a phase recovery algorithm to retrieve time-domain signals. In particular, the multiple input spectrogram inversion (MISI) algorithm has shown good performance in several recent works. This algorithm minimizes a quadratic reconstruction error between magnitude spectrograms.

Magnitude spectrum-based features are the most widely employed front-ends for acoustic modelling in automatic speech recognition (ASR) systems. In this paper, we investigate the possibility and efficacy of acoustic modelling using the raw short-time phase spectrum. In particular, we study the usefulness of the raw wrapped, unwrapped and minimum-phase phase spectra as well as the phase of the source and filter components for acoustic modelling.

Noise reduction in B-format recordings is particularly challenging as it concurrently requires to suppress undesired signals and preserve the spatial properties of the acoustic environment. In particular, wind noise poses an undesirable acoustic condition outdoors. In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors.

Noise reduction in B-format recordings is particularly challenging as it concurrently requires to suppress undesired signals and preserve the spatial properties of the acoustic environment. In particular, wind noise poses an undesirable acoustic condition outdoors. In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors.