Michael Neri

Università Degli Studi Roma Tre | UNIROMA3 · Department of Industrial, Electronic and Mechanical Engineering

Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling. Most studies predominantly center on employing a classification approach, where distances are discretized into distinct categories, enabling smoother model training and achieving higher accuracy...

The research conducted within the audio signal processing field is increasingly focusing on environmental sound classification. This paper presents a low-complexity Fully Con-volutional Network composed of two parallel branches. These branches are responsible for extracting features from the Cadence Frequency Diagram representation and the Chebyche...

We introduce the novel task of continuous-valued speaker distance estimation which focuses on estimating non-discrete distances between a sound source and microphone, based on audio captured by the microphone. A novel learning-based approach for estimating speaker distance in reverberant environments from a single omnidi-rectional microphone is pro...

Artificial Intelligence techniques are being applied in the quality assessment of immersive multimedia content, such as virtual and augmented reality scenarios. The immersive nature of these applications poses a unique challenge to traditional quality assessment methods. In fact, estimating user acceptance of immersive technologies is complex due t...

In the near future, the broadcasting scenario will be characterized by immersive content. One of the systems for capturing the 3D content of a scene is the Light Field imaging. The huge amount of data and the specific transmission scenario impose strong constraints on services and applications. Among others, the evaluation of the quality of the rec...

The objective of a sound event detector is to recognize anomalies in an audio clip and return their onset and offset. However, detecting sound events in noisy environments is a challenging task. This is due to the fact that in a real audio signal several sound sources co-exist and that the characteristics of polyphonic audio are different from isol...

Deep learning models allow the creation of deepfake synthetic audios which are difficult to distinguish from natural ones. Moreover, recognizing which algorithm generated a given synthetic audio is even more challenging. This challenging task, scarcely explored in the literature, is the focus of this paper. We introduce a deep learning approach to...

Accurate detection and classification of objects in 3D point clouds is a central problem in several applications such as autonomous navigation and augmented/virtual reality scenarios. In this paper we present a deep learning strategy for 3D object detection for railway applications based on the VoxelNet model. Due to the lack of publicly available...

This paper proposes a machine learning-based architecture for audio signals classification based on a joint exploitation of the Chebychev moments and the Mel-Frequency Cepstrum Coefficients. The procedure starts with the computation of the Mel-spectrogram of the recorded audio signals; then, Chebychev moments are obtained projecting the Cadence Fre...

Satellite-based positioning has been selected as one of the key game changers for the evolution of the European Rail Traffic Management System, introducing strict accuracy, integrity and continuity requirements. However, GNSSs are vulnerable to several degradations that impair the fulfillment of the performance demands. For this reason, we propose...