5 accepted papers
In this contribution, we introduce a novel approach to noise-robust acoustic echo cancellation employing a complex-valued Deep Neural Network (DNN) for postfiltering. In a first step, early linear echo components are removed using a double-talk robust adaptive filter. The residual signal is subseque…
Due to manufacturing tolerances, the geometry parameters of workpieces are not constant in industrial welding applications. Today, this problem is addressed by either accepting fluctuating part quality or by measuring the geometry and adjusting the configuration of the robot and process controller f…
The suppression of ego-noise for (humanoid) robots is typically addressed by learning-based techniques. In this paper, we propose a novel approach which models significant parts of ego-noise spectrograms based on motor data and does not require a prior training step. Accordingly, the intrinsic harmo…
The use of autonomous systems (ASs), such as humanoid robots, drones or self-driving vehicles, has expanded significantly in recent years. For such systems, acoustic scene analysis can provide useful information about the environment and supports the AS to react appropriately. However, compared to m…
We address the problem of ego-noise reduction, i.e., suppressing the noise a robot causes by its own motions. Such noise degrades the recorded microphone signal massively such that the robot's auditory capabilities suffer. To suppress it, it is intuitive to use also motor data, since it provides add…