Software

 

The provided software is available as is, without warranty of any kind. Please, cite the related paper when using the code.


Acoustic event detection

Matlab code for baseline system on acoustic event detection, developed as part of the IEEE D-CASE Challenge. Developed by Dimitrios Giannoulis and Emmanouil Benetos, as presented in

D. Giannoulis, D. Stowell, E. Benetos, M. Rossignol, M, Lagrange, and M.D. Plumbley, A database and challenge for acoustic scene classification and event detection,in Proc. of the 21st European Signal Processing Conf., Marrakech, Morocco, 20-25 June 2013


Color Filter Arrays: representation, analysis and a design methodology

The code generates frequency structure, multiplexing matrix and demosaicking matrix for analysis of a given Color Filter Array pattern, as presented in

P. Hao, Y. Li, Z. Lin, E. Dubois, A geometric method for optimal design of Color Filter Arrays, IEEE Trans. on Image Processing, Vol. 20, Issue 3, pp. 709-722, March 2011


Dialogue Similarity calculation tools

This code implements a set of tools for calculating similarity between speakers in dialogue, across standard and randomised corpora, as presented in

C. Howes, P. G. T. Healey, and M. Purver, Tracking lexical and syntactic alignment in conversation, in Proc. of Annual Conf. of the Cognitive Science Society, Portland, Oregon, USA, 15-19 August 2010


Distance blurring for space-variant image coding

The code performs the depth-based blurring described in

T. Popkin, A. Cavallaro and D. Hands, Image coding using depth blurring for aesthetically acceptable distortion, IEEE Trans. Image Processing, Vol. 20, Issue 11, November 2011


DyLan (Dynamics of Language) dialogue system and toolkit

This code implements a Dynamic Syntax parser and generator for the English language, within a word-by-word incremental dialogue system for the travel domain, as described in

M. Purver, A. Eshghi, and J. Hough, Incremental semantic construction in a Dialogue System, Proc. of Int. Conf. on Computational Semantics, Oxford, UK, 12-14 January 2011


Efficient depth blurring with occlusion handling

The code performs the depth-based blurring described in

T. Popkin, A. Cavallaro and D. Hands, Efficient depth blurring with occlusion handling, in Proc. of IEEE Int. Conf. on Image Processing, Brussels, Belgium, 11-14 September 2011


GM-PHD filter implementation (Gaussian mixture probability hypothesis density filter)

This Pyton code implements the paper Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter, as presented in

D. Stowell and M. D. Plumbley, Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter, in Int. Workshop on Machine Learning and Music, Edinburgh, UK, 30 June 2012


Landmark localization and registration for 3D faces

This software registers 3D faces and calculates their differences using the algorithms described in

P. Nair and A. Cavallaro, 3D face detection, landmark localization and registration using a Point Distribution Model, IEEE Trans. on Multimedia, Vol. 11, No. 4, June 2009


Max-Margin Semi-NMF

This code implements the paper Max-Margin Semi-NMF (MNMF), as presented in

V. Kumar, I. Kotsia and I. Patras, Max-Margin Semi-NMF, British Machine Vision Conf., Dundee, UK, 29 August - 2 September 2011


Multi-feature object trajectory clustering

The code performs the clustering procedure described in

N. Anjum and A. Cavallaro, Multi-feature object trajectory clustering for video analysis, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 18, Issue 11, pp. 1555-1564, November 2008


Multi-foveation filtering

The code performs the spatial filtering described in

T. Popkin, A. Cavallaro and D. Hands, Multi-foveation filtering, in Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 19-24 April 2009


Protocol for evaluating trackers (PFT)

The code reproduces the evaluation protocol described in

T. Nawaz and A. Cavallaro, PFT: A protocol for evaluating video trackers, in Proc. of IEEE Int. Conf. on Image Processing, Brussels, Belgium, 11-14 September 2011


Probabilistic Subpixel Temporal Registration for Facial Expression Analysis

This code implements the PSTR technique for sequence registration as presented in

E. Sariyanidi, H. Gunes and A. Cavallaro, Probabilistic subpixel temporal registration for facial expression analysis, in Proc. of the Asian Computer Vision Conf., Singapore, 1-5 November 2014


Quantised Local Zernike Moments

This code implements the Quantised Local Zernike Moments (QLZM) image representation, as presented in

E. Sariyanidi, H. Gunes, M. Gokmen and A. Cavallaro, Local Zernike Moment representation for facial affect recognition , British Machine Vision Conf., Bristol, UK, 9-13 September 2013


Sentimental

These APIs implement Chatterbox's sentiment/emotion detection tools (free to use below a certain data limit), based around the method described in

M. Purver and S. Battersby, Experimenting with distant supervision for emotion classification, Proc. of Conf. of the European Chapter of the Association for Computational Linguistics, Avignon, France, 23-27 April 2012


Space-variant Gaussian blurring

The code performs the spatial blurring described in

T. Popkin, A. Cavallaro and D. Hands, Accurate and efficient method for smoothly space-variant Gaussian blurring, IEEE Trans. Image Processing, Vol. 19, No. 5, pp. 1362-1370, May 2010


Support Tucker Machines

This code implements Support Tucker Machines (STuMs) and Sw-STuMs as presented in,

I. Kotsia and I. Patras, Support Tucker Machines, in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Colorado Springs, Colorado, USA, 21-25 June 2011


Tensor Regression

This code implements Support Tensor Regression (STR), as presented in

W. Guo, I. Kotsia and I. Patras, Tensor Learning for Regression, IEEE Trans. on Image Processing, Vol. 21, No. 2, pp. 816-827, February 2012


Xamrt - cross-associative tree regression algorithm

This code implements the paper Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression, as presented in

D. Stowell and M. D. Plumbley, Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression, Journal of New Music Research, Vol. 40, No. 4, pp. 325-336, 2014



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