38 citations · 66 across the 22 of their papers we have counts for
22 papers
Learning from Audio-Dependency Errors: Data Curation Strategies Based on Model Confusion Patterns in Audio Question Answering
Hyeonuk Nam
We frame the system as diagnostic data curation for a large audio-language model: before fine-tuning, we probe Qwen3-Omni-30B-A3B-Instruct under normal, empty-audio, and shuffled-a…
Auditory Intelligence: Understanding the World Through Sound
Hyeonuk Nam
Recent progress in auditory intelligence has yielded high-performing systems for sound event detection (SED), acoustic scene classification (ASC), automated audio captioning (AAC),…
Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots
Gyeong-Tae Lee, Hyeonuk Nam, Yong-Hwa Park
This paper introduces Binaural Sound Event Localization and Detection (BiSELD), a task that aims to jointly detect and localize multiple sound events using binaural audio, inspired…
Frequency Dynamic Convolutions for Sound Event Detection
Hyeonuk Nam
Recent research in deep learning-based Sound Event Detection (SED) has primarily focused on Convolutional Recurrent Neural Networks (CRNNs) and Transformer models. However, convent…
DNN based HRIRs Identification with a Continuously Rotating Speaker Array
Byeong-Yun Ko, Deokki Min, Hyeonuk Nam +1
Conventional static measurement of head-related impulse responses (HRIRs) is time-consuming due to the need for repositioning a speaker array for each azimuth angle. Dynamic approa…
Temporal Attention Pooling for Frequency Dynamic Convolution in Sound Event Detection
Hyeonuk Nam, Yong-Hwa Park
Recent advances in deep learning, particularly frequency dynamic convolution (FDY conv), have significantly improved sound event detection (SED) by enabling frequency-adaptive feat…
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection
Hyeonuk Nam, Yong-Hwa Park
Sound event detection (SED) has significantly benefited from self-supervised learning (SSL) approaches, particularly masked audio transformer for SED (MAT-SED), which leverages mas…
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…
Self Training and Ensembling Frequency Dependent Networks with Coarse Prediction Pooling and Sound Event Bounding Boxes
Hyeonuk Nam, Deokki Min, Seungdeok Choi +2
To tackle sound event detection (SED), we propose frequency dependent networks (FreDNets), which heavily leverage frequency-dependent methods. We apply frequency warping and Filter…
Pushing the Limit of Sound Event Detection with Multi-Dilated Frequency Dynamic Convolution
Hyeonuk Nam, Yong-Hwa Park
Frequency dynamic convolution (FDY conv) has been a milestone in the sound event detection (SED) field, but it involves a substantial increase in model size due to multiple basis k…
Auditory Neural Response Inspired Sound Event Detection Based on Spectro-temporal Receptive Field
Deokki Min, Hyeonuk Nam, Yong-Hwa Park
Sound event detection (SED) is one of tasks to automate function by human auditory system which listens and understands auditory scenes. Therefore, we were inspired to make SED rec…
VIFS: An End-to-End Variational Inference for Foley Sound Synthesis
Junhyeok Lee, Hyeonuk Nam, Yong-Hwa Park
The goal of DCASE 2023 Challenge Task 7 is to generate various sound clips for Foley sound synthesis (FSS) by "category-to-sound" approach. "Category" is expressed by a single inde…
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…