The Living Thing / Notebooks : Synestizer

I am working on an project with with @kultkat and @stahlnow to bring easy sonification of visuals to the masses.

Here is the main sourcecode repository, and here is the online prototype. You are invited to play with it yourself, and contribute to development.

Refs

Achl03
Achlioptas, D. (2003) Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences, 66(4), 671–687. DOI.
AiCh09
Ailon, N., & Chazelle, B. (2009) The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors. SIAM Journal on Computing, 39(1), 302–322. DOI.
AlMa14
Alaoui, A. E., & Mahoney, M. W.(2014) Fast Randomized Kernel Methods With Statistical Guarantees. arXiv:1411.0306 [Cs, Stat].
Fodo02
Fodor, I. (2002) A Survey of Dimension Reduction Techniques.
GeLR16
Gel, Y. R., Lyubchich, V., & Ramirez, L. L.(2016) Fast Patchwork Bootstrap for Quantifying Estimation Uncertainties in Sparse Random Networks.
GiSB16
Giryes, R., Sapiro, G., & Bronstein, A. M.(2016) Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?. IEEE Transactions on Signal Processing, 64(13), 3444–3457. DOI.
HaKD13
Hawe, S., Kleinsteuber, M., & Diepold, K. (2013) Analysis operator learning and its application to image reconstruction. IEEE Transactions on Image Processing, 22(6), 2138–2150. DOI.
HeWH16
He, K., Wang, Y., & Hopcroft, J. (2016) A Powerful Generative Model Using Random Weights for the Deep Image Representation. arXiv:1606.04801 [Cs].
HRKC16
Hong, S., Roh, B., Kim, K.-H., Cheon, Y., & Park, M. (2016) PVANet: Lightweight Deep Neural Networks for Real-time Object Detection. arXiv:1611.08588 [Cs].
KaNe14
Kane, D. M., & Nelson, J. (2014) Sparser Johnson-Lindenstrauss Transforms. Journal of the ACM, 61(1), 1–23. DOI.
LaNK87
Lahat, M., Niederjohn, R. J., & Krubsack, D. (1987) A spectral autocorrelation method for measurement of the fundamental frequency of noise-corrupted speech. IEEE Transactions on Acoustics, Speech and Signal Processing, 35(6), 741–750. DOI.
LiHC06
Li, P., Hastie, T. J., & Church, K. W.(2006) Very Sparse Random Projections. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 287–296). New York, NY, USA: ACM DOI.
MaMu12
Maillard, O.-A., & Munos, R. (2012) Linear regression with random projections. Journal of Machine Learning Research, 13(Sep), 2735–2772.
NaTB10
Naseem, I., Togneri, R., & Bennamoun, M. (2010) Linear regression for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 2106–2112.
OyTr15
Oymak, S., & Tropp, J. A.(2015) Universality laws for randomized dimension reduction, with applications. arXiv:1511.09433 [Cs, Math, Stat].
PaHC15
Patraucean, V., Handa, A., & Cipolla, R. (2015) Spatio-temporal video autoencoder with differentiable memory. arXiv:1511.06309 [Cs].
RaRe07
Rahimi, A., & Recht, B. (2007) Random features for large-scale kernel machines. In Advances in neural information processing systems (pp. 1177–1184). Curran Associates, Inc.
RMAR07
Rasmussen, J. G., Møller, J., Aukema, B. H., Raffa, K. F., & Zhu, J. (2007) Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(4), 701–713. DOI.
SoTa10
Song, D., & Tao, D. (2010) Biologically inspired feature manifold for scene classification. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 19(1), 174–184. DOI.
ZhFX11
Zhao, B., Fei-Fei, L., & Xing, E. P.(2011) Online detection of unusual events in videos via dynamic sparse coding. In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3313–3320). DOI.