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Compared to the statistical model approach, the neural approach focuses on learning frame-level speaker features, hence more suitable for dealing with short speech segments,
We develop a pipeline catered to this task combining adaptive noise removal, non-speech filtering and new measures of conflict based on the repetition and intensity of phrases
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event
The probability of assigning the most recent data point to any of the already generated states is proportional to the number of observation elements which have been assigned to
We train a monaural listen, attend and spell (LAS) model [28] on the clean WSJ training data as described in Section 4.1, performing early stopping by the WER of the model on
We validate our framework with 44 experiments in five different areas on our campus ( 3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and
Once, the audio signal (regarding the stimulus in the test set) is reconstructed using the EEG features, we calculate the Pearson correlation between the spec- trogram of the
A similar approach was used in [41] in the context of MIMO synthetic aperture radar (SAR) with orthogonal frequency-division multiplexing linear frequency modulated (OFDM
