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    Telecommunications and Multimedia

    At CTM, our vision is to promote a lively and sustainable world where networked intelligence enables ubiquitous interaction with sensory-rich content. Our mission is to develop advanced systems and technologies to enable high capacity, efficient, and secure communications, media knowledge extraction, and immersive ubiquitous multimedia applications.

    We work in 4 main areas of research: Optical and Electronic Technologies, Wireless Networks, Multimedia and Communications Technologies, and VCMI (Visual Computing and Machine Intelligence).

    Latest News

    INESC TEC with five FCT exploratory projects approved in four R&D areas

    Telecommunications and Multimedia, Applied Photonics, High-assurance Software and Advanced Computing Systems – these are the four domains that INESC TEC researchers will explore within the scope of the five projects that were approved through the Call for Exploratory Projects promoted by the Foundation for Science and Technology (FCT).

    02nd October 2024

    Artificial Intelligence

    Já arrancou o primeiro projeto europeu liderado pelo INESC TEC na área da saúde

    Chama-se AI4Lungs e tem como objetivo desenvolver ferramentas e modelos computacionais baseados em Inteligência Artificial para otimizar o diagnóstico e o tratamento de doenças pulmonares. Através de uma abordagem holística e multimodal, os investigadores vão criar uma solução de cuidados de saúde personalizados para doenças respiratórias. No final de fevereiro, representantes das 18 entidades parceiras do projeto, provenientes de 10 países, reuniram-se no INESC TEC para assinalar o arranque do AI4Lungs.

    01st April 2024

    Communications

    Europe discusses collaboration opportunities in high-frequency wireless communications

    Smart propagation environments, improvements in signal processing for the sixth generation of mobile communications, and 6G-centred network and location developments were some of the topics discussed at an event organised by the European projects TERRAMETA (coordinated by INESC TEC), 6G-SHINE and TIMES, in collaboration with RESTART-IN – an Italian PRR.

    06th March 2024

    Artificial Intelligence

    INESC TEC researchers work on the first prototype that applies AI to colorectal diagnosis developed in Portugal

    The work behind the first prototype that uses Artificial Intelligence (AI) for colorectal diagnosis was fully developed by Portuguese researchers INESC TEC, and the IMP Diagnostics Molecular & Anatomic Pathology laboratory; the work featured in the renowned international scientific journal npj Precision Oncology (https://www.nature.com/articles/s41698-024-00539-4 ).

    05th March 2024

    INESC TEC researchers led discussion on wireless communications and computer vision at GLOBECOM

    After almost one year, the CONVERGE project (coordinated by INESC TEC) has already showed relevant outcomes at one of the main conferences of the IEEE Communications Society, the GLOBECOM (Malaysia) – namely, through the organisation of a panel. “Convergence of wireless communications and computer vision: a new paradigm created by the CONVERGE project” sought to discuss the new opportunities and potential challenges associated with the use of tools that combine radio with computer vision.

    23rd January 2024

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    Featured Projects

    PFAI4_5eD

    Programa de Formação Avançada Industria 4 - 5a edição

    2024-2024

    Team
    Vítor Grade Tavares

    Centre Coordinator

    Filipe André Ribeiro

    Centre Coordinator

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    002

    Laboratories

    Laboratory of Sound and Music Computing

    Optical and Electronic Technologies Research Laboratory

    Publications

    CTM Publications

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    2025

    A Review of Voicing Decision in Whispered Speech: From Rules to Machine Learning

    Authors
    da Silva, JMPP; Duarte Nunes, G; Ferreira, A;

    Publication

    Abstract

    2025

    Neural network models for whisper to normal speech conversion

    Authors
    Yamamura, F; Scalassara, R; Oliveira, A; Ferreira, JS;

    Publication
    U.Porto Journal of Engineering

    Abstract
    Whispers are common and essential for secondary communication. Nonetheless, individuals with aphonia, including laryngectomees, rely on whispers as their primary means of communication. Due to the distinct features between whispered and regular speech, debates have emerged in the field of speech recognition, highlighting the challenge of effectively converting between them. This study investigates the characteristics of whispered speech and proposes a system for converting whispered vowels into normal ones. The system is developed using multilayer perceptron networks and two types of generative adversarial networks. Three metrics are analyzed to evaluate the performance of the system: mel-cepstral distortion, root mean square error of the fundamental frequency, and accuracy with f1-score of a vowel classifier. Overall, the perceptron networks demonstrated better results, with no significant differences observed between male and female voices or the presence/absence of speech silence, except for improved accuracy in estimating the fundamental frequency during the conversion process. © 2025, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

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    2025

    A Vision-aided Open Radio Access Network for Obstacle-aware Wireless Connectivity

    Authors
    Simões, C; Coelho, A; Ricardo, M;

    Publication
    20th Wireless On-Demand Network Systems and Services Conference, WONS 2025, Hintertux, Austria, January 27-29, 2025

    Abstract

    2025

    A Framework to Develop and Validate RL-Based Obstacle-Aware UAV Positioning Algorithms

    Authors
    Shafafi, K; Ricardo, M; Campos, R;

    Publication
    CoRR

    Abstract

    2025

    Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables

    Authors
    Caetano, R; Oliveira, JM; Ramos, P;

    Publication
    MATHEMATICS

    Abstract
    Accurate demand forecasting is essential for retail operations as it directly impacts supply chain efficiency, inventory management, and financial performance. However, forecasting retail time series presents significant challenges due to their irregular patterns, hierarchical structures, and strong dependence on external factors such as promotions, pricing strategies, and socio-economic conditions. This study evaluates the effectiveness of Transformer-based architectures, specifically Vanilla Transformer, Informer, Autoformer, ETSformer, NSTransformer, and Reformer, for probabilistic time series forecasting in retail. A key focus is the integration of explanatory variables, such as calendar-related indicators, selling prices, and socio-economic factors, which play a crucial role in capturing demand fluctuations. This study assesses how incorporating these variables enhances forecast accuracy, addressing a research gap in the comprehensive evaluation of explanatory variables within multiple Transformer-based models. Empirical results, based on the M5 dataset, show that incorporating explanatory variables generally improves forecasting performance. Models leveraging these variables achieve up to 12.4% reduction in Normalized Root Mean Squared Error (NRMSE) and 2.9% improvement in Mean Absolute Scaled Error (MASE) compared to models that rely solely on past sales. Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments.

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    Facts & Figures

    82Researchers

    2016

    19Papers in indexed journals

    2020

    15Academic Staff

    2020

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    Contacts
    Filipe André Ribeiro

    Coordinator

    +351222094299

    filipe.a.ribeiro@inesctec.pt