no code implementations • 14 Jun 2023 • David Diaz-Guerra, Archontis Politis, Antonio Miguel, Jose R. Beltran, Tuomas Virtanen
Conventional recurrent neural networks (RNNs), such as the long short-term memories (LSTMs) or the gated recurrent units (GRUs), take a vector as their input and use another vector to store their state.
2 code implementations • 31 Mar 2022 • David Diaz-Guerra, Antonio Miguel, Jose R. Beltran
In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array.
no code implementations • 6 Nov 2021 • Victoria Mingote, Antonio Miguel, Alfonso Ortega, Eduardo Lleida
This paper explores three novel approaches to improve the performance of speaker verification (SV) systems based on deep neural networks (DNN) using Multi-head Self-Attention (MSA) mechanisms and memory layers.
no code implementations • 27 Oct 2021 • Pablo Gimeno, Victoria Mingote, Alfonso Ortega, Antonio Miguel, Eduardo Lleida
Area under the ROC curve (AUC) optimisation techniques developed for neural networks have recently demonstrated their capabilities in different audio and speech related tasks.
2 code implementations • 16 Jun 2020 • David Diaz-Guerra, Antonio Miguel, Jose R. Beltran
In this paper, we present a new single sound source DOA estimation and tracking system based on the well-known SRP-PHAT algorithm and a three-dimensional Convolutional Neural Network.
no code implementations • 31 Jan 2019 • Victoria Mingote, Antonio Miguel, Alfonso Ortega, Eduardo Lleida
This paper explores two techniques to improve the performance of text-dependent speaker verification systems based on deep neural networks.
no code implementations • 27 Jan 2019 • Javier Antoran, Antonio Miguel
Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models.
no code implementations • 3 Jan 2019 • Dayana Ribas, Jorge Llombart, Antonio Miguel, Luis Vicente
The DNN model, trained with artificial synthesized reverberation data, was able to deal with far-field reverberated speech from real scenarios.
no code implementations • 27 Dec 2018 • Antonio Miguel, Jorge Llombart, Alfonso Ortega, Eduardo Lleida
As in Joint Factor Analysis, the model uses tied hidden variables to model speaker and session variability and a MAP adaptation of some of the parameters of the model.
no code implementations • 22 Dec 2018 • Victoria Mingote, Antonio Miguel, Alfonso Ortega, Eduardo Lleida
Moreover, we can apply a convolutional neural network as front-end, and thanks to the alignment process being differentiable, we can train the whole network to produce a supervector for each utterance which will be discriminative with respect to the speaker and the phrase simultaneously.
3 code implementations • 26 Oct 2018 • David Diaz-Guerra, Antonio Miguel, Jose R. Beltran
The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed.