754 citations · 3.2k across the 112 of their papers we have counts for
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Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks
Emad M. Grais, Hagen Wierstorf, Dominic Ward +2
Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals. Therefore, common performance evaluation…
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…
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…
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
Emad M. Grais, Dominic Ward, Mark D. Plumbley
Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is…