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The Python codes for the implementation of the deep learning baselines can be downloaded from Qiuqiang Kong's Github page:
Key references: Q. Kong, I. Sobieraj, W. Wang and M. D. Plumbley, "Deep Neural Network Baseline for DCASE Challenge 2016," in DCASE2016 workshop. [PDF]
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Key references: Y. Yu, W. Wang, and P. Han, "Localization based stereo speech source separation using probabilistic time-frequency masking and deep neural networks", EURASIP Journal on Audio Speech and Music Processing, 2016:7, 18 pages, DOI 10.1186/s13636-016-0085-x, 2016. [PDF]
Key references: V. Kilic, M. Barnard, W. Wang, and J. Kittler, "Audio assisted robust visual tracking with adaptive particle filtering", IEEE Transactions on Multimedia, vol. 17, no. 2, pp. 186-200, 2015. [PDF]
Key references: V. Kilic, M. Barnard, W. Wang, A. Hilton, and J. Kittler, "Mean-Shift and Sparse Sampling Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking", IEEE Transactions on Multimedia, vol. 18, no. 10, October 2016. [PDF]
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[Home] [Publications] [Research] [Teaching] [Short Bio] [Demo & Data] [Codes]
Free Software Toolboxes
Sparse Representation & Dictionary Learning Algorithms with Applications in Denoising, Separation, Localisation and Tracking
SimCO: sparse synthesis model based dictionary learning algorithms and their applications in image denoising
The Matlab codes and the details can be found from here.
Analysis SimCO: sparse analysis model based dictionary learning algorithms and their applications in image denoising
The Matlab codes can be found from here.
Sparse analysis model based multiplicative noise removal
The Matlab codes can be found from Jing Dong's Github page in here.
Consistent dictionary learning for audio declipping
The Matlab codes can be found from Lucas Rencker's Github page or personal page
Audio-visual dictionary learning and its application to multimodal source separation
Download the Matlab codes: slim version (5.7M, including core codes, and test example using synthetic data), and full version (1.3G, including core codes, test examples and comprehensive test results shown in the paper).
Deep Learning Algorithms with Applications in Classification, Detection, Tagging, and Separation
Deep learning baselines for DCASE challenge 2016
Hierarchical DNN for acoustic scene classification
The C/C++ codes can be found from Yong Xu's Github page in here.
Fully deep neural networks for audio tagging
The C/C++ codes can be found from Yong Xu's Github page in here.
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
The Python codes can be found from Yong Xu's Github page in here.
Deep learning for sterero speech separation
The Matlab codes can be found from here (to be added soon).
Particle Filtering, PHD Filtering, & Particle Flow Algorithms with Applications in Multimodal Fusion and Tracking
Adaptive particle filtering for audio-visual tracking of multiple speakers
The Matlab codes, demos and the details can be found from here.
PHD filtering, Mean-Shift PHD filtering, and Sparse Sampling MS-PHD filtering for audio-visual tracking of multiple speakers
The Matlab codes, demos, and details can be found from here.
Convolutive ICA, NMF, Time-Frequency Masking with applications in Blind Source Separation & Computational Auditory Scene Analysis
Underdetermined speech source separation based on sparse coding and dictionary learning
The Matlab codes and the details can be found from.
Convolutive speech source separation based on probabilistic time-frequency masking
The Matlab codes and the details can be found from here (to be added soon).
Spatial Audio
Sparse L1-Optimal Multi-Loudspeaker Panning
The Matlab codes and the details can be found from here.