Benjamin Ricaud | EPFL Graph Search

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Learnable filter-banks for CNN-based audio applications

Nicolas Aspert, Laurent Colbois, Helena Peic Tukuljac, Benjamin Ricaud

We investigate the design of a convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications or applications involving time-series. The kernels are defined as one-dimensional functions having a band-pass filter shape, with a limited number of trainable parameters. Building on the literature on this topic, we confirm that networks having such an input layer can achieve state-of-the-art accuracy on several audio classification tasks. We explore the effect of different parameters on the network accuracy and learning ability. This approach reduces the number of weights to be trained and enables larger kernel sizes, an advantage for audio applications. Furthermore, the learned filters bring additional interpretability and a better understanding of the audio properties exploited by the network.

2022

Naviguer dans les traces numériques sur Twitter. Retour sur la conception d’un dispositif de cartographie de données à destination de journalistes

Nicolas Aspert, Vincent Carlino, Benjamin Ricaud

This article provides feedback on a multidisciplinary and applied research project to design a data mapping and exploration tool on Twitter for journalists. The approach focuses on the observation and understanding of journalists' uses and a projection of the integration of the software in their daily practices and routines. Three use cases scenarios are explored, reflecting the diversity of the tool's appropriation. Each of them underlines the interest in designing a transparent interface that favors the user's understanding of the operations performed on the data, but also the need for users to have a minimal digital culture to produce journalistic content based on these digital traces.

2021

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatically sample user-defined relevant parts of a network. Starting from a few selected places in the network and a reduced set of expansion rules, the method adopts a filtered breadth-first search approach, that expands through edges and nodes matching these properties. Moreover, the expansion is performed over a random subset of neighbors at each step to mitigate further the overwhelming number of connections that may exist in large graphs. This carries the image of a “spiky” expansion. We show that this approach generalize previous exploration sampling methods, such as Snowball or Forest Fire and extend them. We demonstrate its ability to capture groups of nodes with high interactions while discarding weakly connected nodes that are often numerous in social networks and may hide important structures.

2020