86 citations · 164 across the 18 of their papers we have counts for
19 papers
PIANO: Personalized Reranking via Information Aggregation Node for Music Search Optimization
Weisheng Li, Chuqiao Huang, Pengcheng Li +5
Unlike short-video content, music tracks have long lifecycles and lasting value. Effective music search re-ranking must therefore align the user's current query with long-term pref…
MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations
Wenxiang Guo, Changhao Pan, Zhiyuan Zhu +16
Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role…
Improving Reliability of Fine-tuning with Block-wise Optimisation
Basel Barakat, Qiang Huang
Finetuning can be used to tackle domain-specific tasks by transferring knowledge. Previous studies on finetuning focused on adapting only the weights of a task-specific classifier…
T-vectors: Weakly Supervised Speaker Identification Using Hierarchical Transformer Model
Yanpei Shi, Mingjie Chen, Qiang Huang +1
Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. This paper proposes a hierarchical network with transformer encoders…
Improving Audio Anomalies Recognition Using Temporal Convolutional Attention Network
Qiang Huang, Thomas Hain
Anomalous audio in speech recordings is often caused by speaker voice distortion, external noise, or even electric interferences. These obstacles have become a serious problem in s…
Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models
Qiang Huang, Thomas Hain
Many applications of speech technology require more and more audio data. Automatic assessment of the quality of the collected recordings is important to ensure they meet the requir…
Speaker Re-identification with Speaker Dependent Speech Enhancement
Yanpei Shi, Qiang Huang, Thomas Hain
While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. Here sp…
Weakly Supervised Training of Hierarchical Attention Networks for Speaker Identification
Yanpei Shi, Qiang Huang, Thomas Hain
Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. In this paper, a hierarchical attention network is proposed to solve…
Robust Speaker Recognition Using Speech Enhancement And Attention Model
Yanpei Shi, Qiang Huang, Thomas Hain
In this paper, a novel architecture for speaker recognition is proposed by cascading speech enhancement and speaker processing. Its aim is to improve speaker recognition performanc…
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Qiang Huang, Jianhui Bu, Weijian Xie +3
Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually refo…
H-VECTORS: Utterance-level Speaker Embedding Using A Hierarchical Attention Model
Yanpei Shi, Qiang Huang, Thomas Hain
In this paper, a hierarchical attention network to generate utterance-level embeddings (H-vectors) for speaker identification is proposed. Since different parts of an utterance may…
Contextual Joint Factor Acoustic Embeddings
Yanpei Shi, Thomas Hain
Embedding acoustic information into fixed length representations is of interest for a whole range of applications in speech and audio technology. Two novel unsupervised approaches…
Improving Noise Robustness In Speaker Identification Using A Two-Stage Attention Model
Yanpei Shi, Qiang Huang, Thomas Hain
While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. To impr…
A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer
Yuncong Li, Zhe Yang, Cunxiang Yin +4
Aspect-category sentiment analysis (ACSA) aims to predict the aspect categories mentioned in texts and their corresponding sentiment polarities. Some joint models have been propose…
Attention and Localization based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging
Yong Xu, Qiuqiang Kong, Qiang Huang +2
Audio tagging aims to perform multi-label classification on audio chunks and it is a newly proposed task in the Detection and Classification of Acoustic Scenes and Events 2016 (DCA…
Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
Yong Xu, Qiuqiang Kong, Qiang Huang +2
Environmental audio tagging is a newly proposed task to predict the presence or absence of a specific audio event in a chunk. Deep neural network (DNN) based methods have been succ…
Hierarchical learning for DNN-based acoustic scene classification
Yong Xu, Qiang Huang, Wenwu Wang +1
In this paper, we present a deep neural network (DNN)-based acoustic scene classification framework. Two hierarchical learning methods are proposed to improve the DNN baseline perf…
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
Yong Xu, Qiang Huang, Wenwu Wang +4
Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper we make contributions to audio t…
Fully DNN-based Multi-label regression for audio tagging
Yong Xu, Qiang Huang, Wenwu Wang +2
Acoustic event detection for content analysis in most cases relies on lots of labeled data. However, manually annotating data is a time-consuming task, which thus makes few annotat…