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Found 8 papers, 0 papers with code
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution
The demand for accurate object detection in aerial imagery has surged with the widespread use of drones and satellite technology.
Is the Ideal Ratio Mask Really the Best? -- Exploring the Best Extraction Performance and Optimal Mask of Mask-based Beamformers
Via the experiments with the CHiME-3 dataset, we verify that the four BFs have the same peak performance as the upper bound provided by the ideal MWF BF, whereas the optimal mask depends on the adopted BF and differs from the IRM.
Metric-based multimodal meta-learning for human movement identification via footstep recognition
We describe a novel metric-based learning approach that introduces a multimodal framework and uses deep audio and geophone encoders in siamese configuration to design an adaptable and lightweight supervised model.
Detecting earthquakes: a novel deep learning-based approach for effective disaster response
In the present study, we present an intelligent earthquake signal detector that provides added assistance to automate traditional disaster responses.
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition
To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization (MNMF).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech.
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
Generative statistical models of chord sequences play crucial roles in music processing.
