24 citations · 34 across the 12 of their papers we have counts for
13 papers
Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception
Yanfeng Shi, Yan Song, Junhui Li +4
Large Audio-Language Models (LALMs) have substantially advanced general audio understanding, yet they remain limited in fine-grained temporal perception, particularly in precise ev…
Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt
Yanfeng Shi, Pengfei Cai, Jun Liu +5
Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, these models still face challenge…
A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators
Lam Pham, Khoi Vu, Dat Tran +4
In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generality of a Deepfake Speech Detection (DSD)…
Environmental Sound Deepfake Detection Using Deep-Learning Framework
Khoi Vu, Dat Tran, Khanh Do +8
In this paper, we propose a deep-learning framework for Environmental Sound Deepfake Detection (ESDD) - the task of identifying whether the sound scene and sound event in an input…
An Efficient Transfer Learning Method Based on Adapter with Local Attributes for Speech Emotion Recognition
Haoyu Song, Ian McLoughlin, Qing Gu +2
Existing speech emotion recognition (SER) methods commonly suffer from the lack of high-quality large-scale corpus, partly due to the complex, psychological nature of emotion which…
Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Pengfei Cai, Yan Song, Qing Gu +3
Most existing sound event detection~(SED) algorithms operate under a closed-set assumption, restricting their detection capabilities to predefined classes. While recent efforts hav…
Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection
Pengfei Cai, Yan Song, Nan Jiang +2
A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs.…
MAT-SED: A Masked Audio Transformer with Masked-Reconstruction Based Pre-training for Sound Event Detection
Pengfei Cai, Yan Song, Kang Li +2
Sound event detection (SED) methods that leverage a large pre-trained Transformer encoder network have shown promising performance in recent DCASE challenges. However, they still r…
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection
Xiao-Min Zeng, Yan Song, Zhu Zhuo +5
In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE)…
AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer
Kang Li, Yan Song, Li-Rong Dai +3
In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio t…
A Light-weight Deep Learning Model for Remote Sensing Image Classification
Lam Pham, Cam Le, Dat Ngo +4
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remo…
Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases
Lam Pham, Huy Phan, Ross King +2
This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are fi…
What Makes Audio Event Detection Harder than Classification?
Huy Phan, Philipp Koch, Fabrice Katzberg +4
There is a common observation that audio event classification is easier to deal with than detection. So far, this observation has been accepted as a fact and we lack of a careful a…