Learning spectro-temporal representations of complex sounds with parameterized neural networks

Learning nonnegative features of spectro-temporal sounds for classification

In this paper we present a method of sound classification which exploits a parts-based representation of spectrotemporal sounds, employing the nonnegative matrix factorization (NMF) [1]. We illustrate a new way of learning nonnegative features using a variant of NMF and show its useful behavior in the task of general sound classification with comparison to independent component analysis (ICA) w...

Spectro-Temporal Analysis of Auscultatory Sounds

Learning Anonymized Representations with Adversarial Neural Networks

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel informationtheoretical bounds. We introduce a novel training objective for simultaneously training ...

Nonnegative features of spectro-temporal sounds for classification

A parts-based representation is a way of understanding object recognition in the brain. The nonnegative matrix factorization (NMF) is an algorithm which is able to learn a parts-based representation by allowing only non-subtractive combinations (Lee and Seung, 1999). In this paper we incorporate a parts-based representation of spectro-temporal sounds into the acoustic feature extraction, which ...

Learning to localise sounds with spiking neural networks

To localise the source of a sound, we use location-specific properties of the signals received at the two ears caused by the asymmetric filtering of the original sound by our head and pinnae, the head-related transfer functions (HRTFs). These HRTFs change throughout an organism’s lifetime, during development for example, and so the required neural circuitry cannot be entirely hardwired. Since H...