Search Results for author:
Found 9 papers, 1 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.
Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation
End-to-end automatic speech recognition (E2E-ASR) has the potential to improve performance, but a specific issue that needs to be addressed is the difficulty it has in handling enharmonic words: named entities (NEs) with the same pronunciation and part of speech that are spelled differently.
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.
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments
By employing a convolutional neural network (CNN)-based multichannel end-to-end speech recognition system, this study attempts to overcome the presents difficulties in everyday environments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Weakly Supervised Deep Recurrent Neural Networks for Basic Dance Step Generation
However, applying DNNs for generating dance to a piece of music is nevertheless challenging, because of 1) DNNs need to generate large sequences while mapping the music input, 2) the DNN needs to constraint the motion beat to the music, and 3) DNNs require a considerable amount of hand-crafted data.
Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition
Emotional aspects play a vital role in making human communication a rich and dynamic experience.
