138 citations · 1.2k across the 72 of their papers we have counts for
9 papers · 1 filter
Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units
Yuanbo Hou, Qiuqiang Kong, Jun Wang +1
Audio tagging aims to detect the types of sound events occurring in an audio recording. To tag the polyphonic audio recordings, we propose to use Connectionist Temporal Classificat…
Weakly supervised CRNN system for sound event detection with large-scale unlabeled in-domain data
Dezhi Wang, Lilun Zhang, Changchun Bao +3
Sound event detection (SED) is typically posed as a supervised learning problem requiring training data with strong temporal labels of sound events. However, the production of data…
General audio tagging with ensembling convolutional neural network and statistical features
Kele Xu, Boqing Zhu, Qiuqiang Kong +4
Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solut…
DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline
Qiuqiang Kong, Turab Iqbal, Yong Xu +2
The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classification and sound event detection tasks: 1) Acoustic scene classification, 2) G…
Sample Mixed-Based Data Augmentation for Domestic Audio Tagging
Shengyun Wei, Kele Xu, Dezhi Wang +3
Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of…
Audio Tagging With Connectionist Temporal Classification Model Using Sequential Labelled Data
Yuanbo Hou, Qiuqiang Kong, Shengchen Li
Audio tagging aims to predict one or several labels in an audio clip. Many previous works use weakly labelled data (WLD) for audio tagging, where only presence or absence of sound…
Capsule Routing for Sound Event Detection
Turab Iqbal, Yong Xu, Qiuqiang Kong +1
The detection of acoustic scenes is a challenging problem in which environmental sound events must be detected from a given audio signal. This includes classifying the events as we…
Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data
Qiuqiang Kong, Yong Xu, Iwona Sobieraj +2
Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip. Many supervised SED algorithms rely on strongly labelled data which contain…
Multi-level Attention Model for Weakly Supervised Audio Classification
Changsong Yu, Karim Said Barsim, Qiuqiang Kong +1
In this paper, we propose a multi-level attention model to solve the weakly labelled audio classification problem. The objective of audio classification is to predict the presence…