Essays about: "recurrent neural network"

Showing result 16 - 20 of 255 essays containing the words recurrent neural network.

1. 16. Graph Neural Networks for Events Detection in Football

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Giovanni Castellano; [2023]
   Keywords : Machine Learning; Graph Neural Network; Recurrent Neural Network; Trajectory Data; Football; Events Detection; Maskininlärning; Graph Neural Network; Recurrent Neural Network; Bandata; fotboll; händelsedetektering;

   Abstract : Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. READ MORE

3. 17. Violin Artist Identification by Analyzing Raga-vistaram Audio

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Nandakishor Ramlal; [2023]
   Keywords : Artist identification; Music information retrieval; Deep Learning; Convolutional Neural Network; Convolutional Recurrent Neural Network; Embeddings; log-Melspectrogram; Artistidentifiering; återhämtning av musikinformation; Deep Learning; Convolutional Neural Network; Convolutional Recurrent Neural Network; Inbäddningar; log-Melspektrogram;

   Abstract : With the inception of music streaming and media content delivery platforms, there has been a tremendous increase in the music available on the internet and the metadata associated with it. In this study, we address the problem of violin artist identification, which tries to classify the performing artist based on the learned features. READ MORE

4. 18. Dataset Drift in Radar Warning Receivers : Out-of-Distribution Detection for Radar Emitter Classification using an RNN-based Deep Ensemble

   University essay from Uppsala universitet/Avdelningen för systemteknik

   Author : Kevin Coleman; [2023]
   Keywords : Radar Emitter Classification; Pulse Descriptor Word; Out of Distribution Detection; Dataset Drift; Uncertainty Estimation; Deep Ensembles; Recurrent Neural Networks; LSTM;

   Abstract : Changes to the signal environment of a radar warning receiver (RWR) over time through dataset drift can negatively affect a machine learning (ML) model, deployed for radar emitter classification (REC). The training data comes from a simulator at Saab AB, in the form of pulsed radar in a time-series. READ MORE

5. 19. Neural Network-Based Residential Water End-Use Disaggregation

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Cajsa Pierrou; [2023]
   Keywords : Residential water end-use; Flow disaggregation; Time series classification; Artificial neural network; Smart water meter; Slutanvändning av vatten i hushåll; Flödesdisaggregering; Tidsserieklassificering; Artificiella neurala nätverk; Smart vattenmätare;

   Abstract : Sustainable management of finite resources is vital for ensuring livable conditions for both current and future generations. Measuring the total water consumption of residential households at high temporal resolutions and automatically disaggregating the sole signal into classified end usages (e.g. READ MORE

6. 20. Evaluation of Methods for Sound Source Separation in Audio Recordings Using Machine Learning

   University essay from Linköpings universitet/Kommunikationssystem

   Author : Amanda Gidlöf; [2023]
   Keywords : Sound source separation; signal processing; audio processing; speech enhancement; speaker identification; speech separation; speech processing; speaker separation; single channel; multi channel; cocktail party problem; machine learning; neural network; deep learning; deep neural network; convolutional network; dual-path network; filter-and-sum network; recurrent network; beamforming;

   Abstract : Sound source separation is a popular and active research area, especially with modern machine learning techniques. In this thesis, the focus is on single-channel separation of two speakers into individual streams, and specifically considering the case where two speakers are also accompanied by background noise. READ MORE