Recent Submissions

  • I-vector transformation using k-nearest neighbors for speaker verification 

    Khan, Umair; India Massana, Miquel Àngel; Hernando Pericás, Francisco Javier (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference report
    Restricted access - publisher's policy
    Probabilistic Linear Discriminant Analysis (PLDA) is the most efficient backend for i-vectors. However, it requires labeled background data which can be difficult to access in practice. Unlike PLDA, cosine scoring avoids ...
  • Weakly supervised semantic segmentation for remote sensing hyperspectral imaging 

    Moliner, Eloi; Salgueiro Romero, Luis Fernando; Vilaplana Besler, Verónica (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference lecture
    Restricted access - publisher's policy
    This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing ...
  • One perceptron to rule them all: language, vision, audio and speech 

    Giró Nieto, Xavier (Association for Computing Machinery (ACM), 2020)
    Conference lecture
    Restricted access - publisher's policy
    Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are ...
  • Automatic reminiscence therapy for dementia 

    Carós, Mariona; Garolera Freixa, Maite; Radeva, Petia; Giró Nieto, Xavier (Association for Computing Machinery (ACM), 2020)
    Conference lecture
    Restricted access - publisher's policy
    With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million ...
  • Improving QoT estimation accuracy with DGE monitoring using machine learning 

    Mahajan, Ankush; Christodoulopoulos, Kostantinos; Martinez, Ricardo; Spadaro, Salvatore; Muñoz, Raul (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference report
    Restricted access - publisher's policy
    In optical transport networks, Dynamic Gain Equalizers (DGE) are typically used at each link. A DGE selectively attenuates the channels to compensate the cumulative Erbium Doped Fiber Amplifier (EDFA) gain ripple effect ...
  • Experimental demonstration of a machine learning-based in-band OSNR estimator from optical spectra 

    Locatelli, Fabiano; Christodoulopoulos, Konstantinos; Fàbrega, Josep Maria; Svaluto Moreolo, Michela; Nadal, Laia; Spadaro, Salvatore (2020)
    Conference report
    Restricted access - publisher's policy
    Channel spectral monitors are becoming a cost effective solution to improve the management, resiliency and efficiency of next generation optical transport networks. We experimentally demonstrate a ...
  • Brown-field gradual migration planning toward spectrally-spatially flexible optical networks 

    Lechowicz, Piotr; Perelló Muntan, Jordi; Spadaro, Salvatore; Walkowiak, Krzysztof (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference report
    Restricted access - publisher's policy
    In this paper, we motivate a brown-field migrationplanning as a cost-efficient procedure to scale the capacity ofshort-term realizable elastic optical networks (EONs), graduallyconverting them into ...
  • Source enumeration via Toeplitz matrix completion 

    Vaibhav, Garg; Giménez Febrer, Pedro Juan; Pagès Zamora, Alba Maria; Santamaria Caballero, Ignacio (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Conference report
    Open Access
    This paper addresses the problem of source enumeration by an array of sensors in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances, referred to non-iid noise ...
  • Feature-based optical spectrum monitoring for failure detection and identification 

    Behnam Shariati, Mohammad; Ruiz Ramírez, Marc; Comellas Colomé, Jaume; Velasco Esteban, Luis Domingo (2019)
    Conference report
    Open Access
    In this paper, we explore the benefits of analysing the optical spectrum of lightpaths for soft-failure detection and identification in Spectrum Switched Optical Network. We present a framework exploiting machine learning ...
  • Impact of traffic delay tolerance on elastic optical networks performance 

    Comellas Colomé, Jaume; Nourmohammadi, Farzaneh; Junyent Giralt, Gabriel (2020)
    Conference report
    Open Access
    Elastic Optical Networks (EON) are considered as a valuable solution to enhance optical networks efficiency due to their superior network spectrum use. By breaking the traditional fixed grid of WDM networks, the spectrum ...
  • Modeling filtering penalties in ROADM-based networks with machine learning for QoT estimation 

    Mahajan, Ankush; Christodoulopoulos, Kostas; Martinez, Ricardo; Spadaro, Salvatore; Muñoz, Raul (2020)
    Conference report
    Open Access
    Monitoring 3dB bandwidth and other spectrum related parameters at ROADMs provides information about quality of their filters. We propose a machine-learning model to estimate end-to-end filtering penalty for more accurate ...
  • Full-field, carrier-less, polarization-diversity, direct detection receiver based on phase retrieval 

    Chen, Haoshuo; Fontaine, Nicolas K.; Gené Bernaus, Joan M.; Ryf, Roland; Neilson, David T.; Raybon, Gregory (The Institution of Engineering and Technology (IET), 2019)
    Conference report
    Open Access
    We realize dual-polarization full-field recovery using intensity only measurements and phase retrieval techniques based on dispersive elements. 30-Gbaud QPSK waveforms are transmitted over 520-km standard single-mode fiber ...

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