Sebastian Stober

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Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12(More)
Current work on Query-by-Singing/Humming (QBSH) focusses mainly on databases that contain MIDI files. Here, we present an approach that works on real audio recordings that bring up additional challenges. To tackle the problem of extracting the melody of the lead vocals from recordings, we introduce a method inspired by the popular “karaoke effect”(More)
Similarity plays an important role in many multimedia retrieval applications. However, it often has many facets and its perception is highly subjective – very much depending on a person’s background or retrieval goal. In previous work, we have developed various approaches for modeling and learning individual distance measures as a weighted linear(More)
We present a prototype system for organization and exploration of music archives that adapts to the user’s way of structuring music collections. Initially, a growing self-organizing map is induced that clusters the music collection. The user has then the possibility to change the location of songs on the map by simple drag-and-drop actions. Each movement of(More)
We introduce and compare several strategies for learning discriminative features from electroencephalography (EEG) recordings using deep learning techniques. EEG data are generally only available in small quantities, they are highdimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording(More)
Sometimes users of a multimedia retrieval system are not able to explicitly state their information need. They rather want to browse a collection in order to get an overview and to discover interesting content. Exploratory retrieval tools support users in search scenarios where the retrieval goal cannot be stated explicitly as a query or user rather want to(More)
Recent approaches in Automatic Image Annotation (AIA) try to combine the expressiveness of natural language queries with approaches to minimize the manual effort for image annotation. The main idea is to infer the annotations of unseen images using a small set of manually annotated training examples. However, typically these approaches suffer from low(More)
Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. In this paper, we present first classification results using deep learning techniques on EEG data recorded within a rhythm perception study in Kigali, Rwanda. We tested 13 adults,(More)
While eye tracking is becoming more and more relevant as a promising input channel, diverse applications using gaze control in a more natural way are still rather limited. Though several researchers have indicated the particular high potential of gaze-based interaction for pointing tasks, often gaze-only approaches are investigated. However, time-consuming(More)