Détail du document
Identifiant
oai:arXiv.org:2408.07234
Sujet
Electrical Engineering and Systems... Computer Science - Artificial Inte...Auteur
Rascon, CalebCatégorie
Computer Science
Année
2024
Date de référencement
15/01/2025
Mots clés
proposed real-time doa qualityRésumé
Real-time speech enhancement has began to rise in performance, and the Demucs Denoiser model has recently demonstrated strong performance in multiple-speech-source scenarios when accompanied by a location-based speech target selection strategy.
However, it has shown to be sensitive to errors in the direction-of-arrival (DOA) estimation.
In this work, a DOA correction scheme is proposed that uses the real-time estimated speech quality of its enhanced output as the observed variable in an Adam-based optimization feedback loop to find the correct DOA.
In spite of the high variability of the speech quality estimation, the proposed system is able to correct in real-time an error of up to 15$^o$ using only the speech quality as its guide.
Several insights are provided for future versions of the proposed system to speed up convergence and further reduce the speech quality estimation variability.
;Comment: Submitted to Digital Signal Processing
Rascon, Caleb, 2024, Direction of Arrival Correction through Speech Quality Feedback
Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain
eeg classify neutral ml participants non-pain expressions images scenes pain
