TY - CHAP A1 - Bayerl, Sebastian P. A1 - Wenninger, Marc A1 - Schmidt, Jochen A1 - Wolff von Gudenberg, Alexander A1 - Riedhammer, Korbinian T1 - STAN: A stuttering therapy analysis helper T2 - 2021 IEEE Spoken Language Technology Workshop (SLT) N2 - Stuttering is a complex speech disorder identified by repetitions, prolongations of sounds, syllables or words and blockswhile speaking. Specific stuttering behaviour differs strongly,thus needing personalized therapy. Therapy sessions requirea high level of concentration by the therapist. We introduce STAN, a system to aid speech therapists in stuttering therapysessions. Such an automated feedback system can lower the cognitive load on the therapist and thereby enable a more consistent therapy as well as allowing analysis of stuttering over the span of multiple therapy sessions. KW - Machine Learning Y1 - 2021 ER - TY - CHAP A1 - Wenninger, Marc A1 - Bayerl, Sebastian P. A1 - Maier, Andreas A1 - Schmidt, Jochen T1 - Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring T2 - 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) N2 - Parameter free Non-intrusive Load Monitoring (NILM) algorithms are a major step toward real-world NILM scenarios. The identification of appliances is the key element in NILM. The task consists of identification of the appliance category and its current state. In this paper, we present a param- eter free appliance identification algorithm for NILM using a 2D representation of time series known as unthresholded Recurrence Plots (RP) for appliance category identification. One cycle of voltage and current (V-I trajectory) are transformed into a RP and classified using a Spacial Pyramid Pooling Convolutional Neural Network architecture. The performance of our approach is evaluated on the three public datasets COOLL, PLAID and WHITEDv1.1 and compared to previous publications. We show that compared to other approaches using our architecture no initial parameters have to be manually tuned for each specific dataset. KW - NILM KW - V-I trajectory KW - Recurrence Plot Y1 - 2021 UR - https://doi.org/10.1109/ICMLA52953.2021.00025 SP - 108 EP - 115 ER - TY - BOOK A1 - Schmidt, Jochen T1 - Grundkurs Informatik – Das Übungsbuch: 163 Aufgaben mit Lösungen N2 - Das Buch richtet sich an Studierende der Informatik oder verwandter Studiengänge und enthält Übungsaufgaben mit Lösungen aus Gebieten, die typischerweise in den ersten Semestern als Grundlagen behandelt werden. Ausgenommen ist der Bereich des Programmierens. Das Buch ergänzt den Grundkurs Informatik mit Übungen zu ausgewählten Kapiteln, ist aber auch in Kombination mit anderen Lehrbüchern verwendbar. KW - Grundlagen Informatik Y1 - 2023 SN - 978-3658434427 U6 - https://doi.org/10.1007/978-3-658-43443-4 PB - Springer Vieweg CY - Wiesbaden ET - 3 ER - TY - CHAP A1 - Hamberger, Anna A1 - Murgul, Sebastian A1 - Schmidt, Jochen A1 - Heizmann, Michael T1 - Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription T2 - Proceedings of the 50th International Computer Music Conference 2025 N2 - Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription. KW - Machine Learning Y1 - 2025 SP - 438 EP - 445 PB - The International Computer Music Association ER -