Speaker: Doroteo Torre Toledano Abstract: The current trend in machine learning assumes that there is a fixed distribution of incoming data, so that a fixed model can be learned to map incoming data to output classes. However, real applications in… Read More
Source Separation for Sound Event Detection in Domestic Environments Using Jointly Trained Models
Speaker: Diego de Benito Gorrón. Abstract: Sound Event Detection and Source Separation are closely related tasks: whereas the first aims to find the time boundaries of acoustic events inside a recording, the goal of the latter is to isolate each… Read More
Representaciones de audio self-supervised Wav2Vec2 para el reconocimiento de locutor
Speaker: Laura Herrera. Abstract: In this Final Degree Project, different speech representations, extracted by unsupervised learning, have been used to train a speaker recognition system. In particular, Wav2Vec2.0 and WavLM features have been used as a novelty. The Wav2Vec2.0 features… Read More
End-to-end deep learning models for air traffic control speech recognition
Speaker: Ana Belén Fernández Cordero. Abstract: For many years, Air Traffic Controllers have had to manually type the information they received and transmitted to pilots into the electronic flight strip systems. This time consuming activity contributed to a significant increase… Read More
Efficient Transformers for End-to-End Neural Speaker Diarization
Speaker: Sergio Izquierdo. Abstract: The recently proposed End-to-End Neural speaker Diarization framework (EEND) handles speech overlap and speech activity detection natively. While extensions of this work have reported remarkable results in both two-speaker and multi-speaker diarization scenarios, these come at… Read More
Sound Event Detection in a large-scale audio dataset with multi-resolution neural networks
Speaker: Sara Barahona Quirós. Abstract: Sound event detection is the task that aims to automatize the human’s ability of recognizing sound events in the environment by their particular acoustic information. For this purpose, deep learning techniques are employed to build… Read More