Tag Archive: paper

Audio inpainting with similarity graphs

I’m very proud to announce the release of a new kind of audio inpainting algorithm. It is able to reconstruct long missing parts of a song by searching through the rest of the content for a suitable replacement.

You can try the algorithm online or download the code here and run it on your machine. A technical report associated with this algorithm is available on arXiv.


In this contribution, we present a method to compensate for long duration data gaps in audio signals, in particular music. To achieve this task, a similarity graph is constructed, based on a short-time Fourier analysis of reliable signal segments, e.g. the uncorrupted remainder of the music piece, and the temporal regions adjacent to the unreliable section of the signal. A suitable candidate segment is then selected through an optimization scheme and smoothly inserted into the gap.

New paper: compressive PCA on graphs



Randomized algorithms reduce the complexity of low-rank recovery methods only w.r.t dimension p of a big dataset YRp×n. However, the case of large n is cumbersome to tackle without sacrificing the recovery. The recently introduced Fast Robust PCA on Graphs (FRPCAG) approximates a recovery method for matrices which are low-rank on graphs constructed between their rows and columns. In this paper we provide a novel framework, Compressive PCA on Graphs (CPCA) for an approximate recovery of such data matrices from sampled measurements. We introduce a RIP condition for low-rank matrices on graphs which enables efficient sampling of the rows and columns to perform FRPCAG on the sampled matrix. Several efficient, parallel and parameter-free decoders are presented along with their theoretical analysis for the low-rank recovery and clustering applications of PCA. On a single core machine, CPCA gains a speed up of p/k over FRPCAG, where k << p is the subspace dimension. Numerically, CPCA can efficiently cluster 70,000 MNIST digits in less than a minute and recover a low-rank matrix of size 10304 X 1000 in 15 secs, which is 6 and 100 times faster than FRPCAG and exact recovery.

New paper: Stationary signal processing on graphs

I’m proud to present a new paper. Using the ideas presented inside, we should be able to improve many graph-based models.


Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or networks. In this paper, we generalize the traditional concept of wide sense stationarity to signals defined over the vertices of arbitrary weighted undirected graphs. We show that stationarity is intimately linked to statistical invariance under a localization operator reminiscent of translation. We prove that stationary graph signals are characterized by a well-defined Power Spectral Density that can be efficiently estimated even for large graphs. We leverage this new concept to derive Wiener-type estimation procedures of noisy and partially observed signals and illustrate the performance of this new model for denoising and regression.