38 citations · 65 across the 11 of their papers we have counts for
11 papers
Towards Understanding of Frequency Dependence on Sound Event Detection
Hyeonuk Nam, Seong-Hu Kim, Deokki Min +2
In this work, we conduct an in-depth analysis of two frequency-dependent methods for sound event detection (SED): FilterAugment and frequency dynamic convolution (FDY conv). The go…
Diversifying and Expanding Frequency-Adaptive Convolution Kernels for Sound Event Detection
Hyeonuk Nam, Seong-Hu Kim, Deokki Min +2
Frequency dynamic convolution (FDY conv) has shown the state-of-the-art performance in sound event detection (SED) using frequency-adaptive kernels obtained by frequency-varying co…
Frequency & Channel Attention for Computationally Efficient Sound Event Detection
Hyeonuk Nam, Seong-Hu Kim, Deokki Min +1
We explore on various attention methods on frequency and channel dimensions for sound event detection (SED) in order to enhance performance with minimal increase in computational c…
Data Augmentation and Squeeze-and-Excitation Network on Multiple Dimension for Sound Event Localization and Detection in Real Scenes
Byeong-Yun Ko, Hyeonuk Nam, Seong-Hu Kim +3
Performance of sound event localization and detection (SELD) in real scenes is limited by small size of SELD dataset, due to difficulty in obtaining sufficient amount of realistic…
Frequency Dependent Sound Event Detection for DCASE 2022 Challenge Task 4
Hyeonuk Nam, Seong-Hu Kim, Deokki Min +3
While many deep learning methods on other domains have been applied to sound event detection (SED), differences between original domains of the methods and SED have not been approp…
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection
Hyeonuk Nam, Seong-Hu Kim, Byeong-Yun Ko +1
2D convolution is widely used in sound event detection (SED) to recognize two dimensional time-frequency patterns of sound events. However, 2D convolution enforces translation equi…
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map
Seong-Hu Kim, Hyeonuk Nam, Yong-Hwa Park
To extract accurate speaker information for text-independent speaker verification, temporal dynamic CNNs (TDY-CNNs) adapting kernels to each time bin was proposed. However, model s…
FilterAugment: An Acoustic Environmental Data Augmentation Method
Hyeonuk Nam, Seong-Hu Kim, Yong-Hwa Park
Acoustic environments affect acoustic characteristics of sound to be recognized by physically interacting with sound wave propagation. Thus, training acoustic models for audio and…
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
Seong-Hu Kim, Hyeonuk Nam, Yong-Hwa Park
In the field of text-independent speaker recognition, dynamic models that adapt along the time axis have been proposed to consider the phoneme-varying characteristics of speech. Ho…
Deep learning based cough detection camera using enhanced features
Gyeong-Tae Lee, Hyeonuk Nam, Seong-Hu Kim +3
Coughing is a typical symptom of COVID-19. To detect and localize coughing sounds remotely, a convolutional neural network (CNN) based deep learning model was developed in this wor…
Heavily Augmented Sound Event Detection utilizing Weak Predictions
Hyeonuk Nam, Byeong-Yun Ko, Gyeong-Tae Lee +4
The performances of Sound Event Detection (SED) systems are greatly limited by the difficulty in generating large strongly labeled dataset. In this work, we used two main approache…