65 citations · 107 across the 15 of their papers we have counts for
17 papers
Emergent Tonal Structure in Learned Chord Embeddings and Its Relation to Tonal Tension
Maral Ebrahimzadeh, Gilberto Bernardes, Sebastian Stober
Several tonal pitch spaces and computational models have been proposed to analyze tonal structure in Western tonal music, many of them grounded in principles from music theory and…
Improving Voice Conversion for Dissimilar Speakers Using Perceptual Losses
Suhita Ghosh, Yamini Sinha, Ingo Siegert +1
The rising trend of using voice as a means of interacting with smart devices has sparked worries over the protection of users' privacy and data security. These concerns have become…
StarGAN-VC++: Towards Emotion Preserving Voice Conversion Using Deep Embeddings
Arnab Das, Suhita Ghosh, Tim Polzehl +1
Voice conversion (VC) transforms an utterance to sound like another person without changing the linguistic content. A recently proposed generative adversarial network-based VC meth…
Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion
Suhita Ghosh, Arnab Das, Yamini Sinha +3
Speech anonymisation prevents misuse of spoken data by removing any personal identifier while preserving at least linguistic content. However, emotion preservation is crucial for n…
Learning Continuous Rotation Canonicalization with Radial Beam Sampling
Johann Schmidt, Sebastian Stober
Nearly all state of the art vision models are sensitive to image rotations. Existing methods often compensate for missing inductive biases by using augmented training data to learn…
Visualizing Deep Neural Networks with Topographic Activation Maps
Valerie Krug, Raihan Kabir Ratul, Christopher Olson +1
Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. However, the complexity of DNNs makes it diffi…
Differentiable Generalised Predictive Coding
André Ofner, Sebastian Stober
This paper deals with differentiable dynamical models congruent with neural process theories that cast brain function as the hierarchical refinement of an internal generative model…
Predictive coding, precision and natural gradients
Andre Ofner, Raihan Kabir Ratul, Suhita Ghosh +1
There is an increasing convergence between biologically plausible computational models of inference and learning with local update rules and the global gradient-based optimization…
PredProp: Bidirectional Stochastic Optimization with Precision Weighted Predictive Coding
André Ofner, Sebastian Stober
We present PredProp, a method for optimization of weights and states in predictive coding networks (PCNs) based on the precision of propagated errors and neural activity. PredProp…
Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data
Indrani Sarkar, Indranil Maji, Charitha Omprakash +3
The assessment of laboratory animal behavior is of central interest in modern neuroscience research. Behavior is typically studied in terms of pose changes, which are ideally captu…
Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond
Anneke Meyer, Suhita Ghosh, Daniel Schindele +4
Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and pe…
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen +10
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the e…
Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models
Andreas Krug, Sebastian Stober
Deep Learning based Automatic Speech Recognition (ASR) models are very successful, but hard to interpret. To gain better understanding of how Artificial Neural Networks (ANNs) acco…
Visualizing Deep Neural Networks for Speech Recognition with Learned Topographic Filter Maps
Andreas Krug, Sebastian Stober
The uninformative ordering of artificial neurons in Deep Neural Networks complicates visualizing activations in deeper layers. This is one reason why the internal structure of such…
Hybrid Active Inference
André Ofner, Sebastian Stober
We describe a framework of hybrid cognition by formulating a hybrid cognitive agent that performs hierarchical active inference across a human and a machine part. We suggest that,…
Transfer Learning for Speech Recognition on a Budget
Julius Kunze, Louis Kirsch, Ilia Kurenkov +3
End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach…
Deep Feature Learning for EEG Recordings
Sebastian Stober, Avital Sternin, Adrian M. Owen +1
We introduce and compare several strategies for learning discriminative features from electroencephalography (EEG) recordings using deep learning techniques. EEG data are generally…