@inproceedings{HambergerMurgulSchmidtetal.2025, author = {Hamberger, Anna and Murgul, Sebastian and Schmidt, Jochen and Heizmann, Michael}, title = {Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription}, series = {Proceedings of the 50th International Computer Music Conference 2025}, booktitle = {Proceedings of the 50th International Computer Music Conference 2025}, publisher = {The International Computer Music Association}, pages = {438 -- 445}, year = {2025}, abstract = {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.}, language = {en} } @article{StecherNeumayerRamachandranetal.2024, author = {Stecher, Dominik and Neumayer, Martin and Ramachandran, Adithya and Hort, Anastasia and Maier, Andreas and B{\"u}cker, Dominikus and Schmidt, Jochen}, title = {Creating a labelled district heating data set: From anomaly detection towards fault detection}, series = {Energy}, volume = {313}, journal = {Energy}, publisher = {Elsevier}, doi = {10.1016/j.energy.2024.134016}, pages = {134016}, year = {2024}, abstract = {For an efficient operation of district heating systems, being able to detect anomalies and faults at an early stage is highly desirable. Here, data-driven machine learning methods can be a cornerstone, particularly for fault detection in district heating substations, where the availability of heat meter data keeps increasing. However, the creation of data sets suitable for training such machine learning models poses challenges to researchers and practitioners alike. To address this problem, we propose a systematic and domain-specific process for data set creation for fault detection in the form of practical guidelines. This process concretizes the data science and data mining cross-industry standard CRISP-DM for the district heating domain and focuses on the process steps of goal definition, data acquisition and understanding, and data curation. We aim to enable researchers and practitioners to create data sets for fault detection in the district heating domain and therefore also enable the creation or improvement of machine learning models in this domain. In addition, we propose a minimum viable feature set for fault detection in district heating networks with the goal of enabling better cooperation between researchers and easier transfer of the resulting machine learning models, to better proliferate new progress in the field.}, language = {en} } @phdthesis{Wenninger2024, author = {Wenninger, Marc}, title = {Demand Response Management Using Machine Learning Methods}, doi = {10.25593/open-fau-448}, school = {Technische Hochschule Rosenheim}, year = {2024}, abstract = {The worldwide transformation of electricity production from fossil and nuclear energy sources to renewable energy sources is accompanied by many challenges. One of those challenges is finding an equilibrium of supply and demand - an important balance for the stability of electric grids. Production and consumption are kept in balance by adapting electricity production to consumption. Most renewable energy sources do not produce energy when demanded, but when natural conditions are suitable. As energy cannot yet be stored efficiently, over-production is as much of a problem as underproduction. Demand Response (DR) is the means for end-users to contribute to the balancing challenge. Providing the end-users with an incentive such as time-based pricing that changes according to the supply will encourage users to contribute to the equilibrium. Users' contribution usually has implications for their daily habits and can be associated with discomfort, thus it requires a high level of involvement. Lowering the required involvement is therefore seen as an important step toward an acceptance of time-based pricing. Since the 1980s, machine learning has been seen as a solution to lower the barrier for private households to participate. The idea is to provide households with information about their electricity consumption, make recommendations on behavior changes or take automated actions. Such information can be retrieved from monitoring a household's electricity consumption. This thesis contributes to the process of extracting information and knowledge from monitored electricity consumption in private households using machine learning. An overview of data sources and general approaches is provided. Based on this research, the Machine Learning Demand Response Model (MLDR) is introduced, defining the relation between data, knowledge, and actions. This model enhances the understanding of the individual steps required to transform monitored electricity consumption data into individual recommendations or automated actions. These steps are: data monitoring, appliance identification, appliance usage segmentation, and appliance usage prediction. For each of these steps, this thesis provides an overview of the current research state and introduces new approaches. A new monitoring system for both individual appliances and household mains is introduced. The system was used to collect a scientific dataset called Domestic Energy Demand Dataset of Individual Appliances in Germany (DEDDIAG). It contains measurements of 50 individual appliances located in 15 homes, recorded with a sample rate of 1 Hz over a period of up to 3.5 years. The dataset has been enriched with manual appliance usage annotations as well as demographic data describing the household. The system, as well as the dataset, has been published under an open-source license. Based on this dataset, an appliance category identification algorithm is introduced. The algorithm extracts features using a wavelet transformation and classifies data using the k-Nearest-Neighbor (kNN) classifier. It was evaluated and published as a challenge baseline for DEDDIAG. Next to this approach that relies on low sample rates, a high sample rate algorithm is introduced. The algorithm is based on transforming one voltage-current cycle, known as the voltage-current (V-I) trajectory, into two separate Recurrence Plots (RPs) which are then classified using a Convolutional Neural Network (CNN) in combination with Spacial Pyramid Pooling (SPP). The al-gorithm is evaluated on three different datasets and compared to previously proposed algorithms. Finding the start and stop of an appliance is the basis for deriving usage patterns. This appliance event segmentation has received little attention from other researchers, and the most commonly used algorithm, a lower-bound thresholding approach, has never been evaluated. Using the manual annotations created for DEDDIAG, this approach is evaluated using a newly introduced performance metrics called Jaccard-Time-Span-Event-Score (JTES). Together with this, a new segmentation algorithm using Support Vector Machine (SVM) is presented. Finally, based on the usage events that were determined, a combined statistical model for appliance usage prediction is introduced. It predicts future appliance usage based on the preferred time of day and the elapsed time since it was used last. It is evaluated on the GREEND dataset as well as the DEDDIAG. The thesis concludes with an outlook of potential future work.}, language = {en} } @inproceedings{BayerlWenningerSchmidtetal.2021, author = {Bayerl, Sebastian P. and Wenninger, Marc and Schmidt, Jochen and Wolff von Gudenberg, Alexander and Riedhammer, Korbinian}, title = {STAN: A stuttering therapy analysis helper}, series = {2021 IEEE Spoken Language Technology Workshop (SLT)}, booktitle = {2021 IEEE Spoken Language Technology Workshop (SLT)}, pages = {2}, year = {2021}, abstract = {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.}, language = {en} } @article{WenningerMaierSchmidt2021, author = {Wenninger, Marc and Maier, Andreas and Schmidt, Jochen}, title = {DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany}, series = {Scientific Data}, volume = {8}, journal = {Scientific Data}, number = {176}, pages = {15}, year = {2021}, abstract = {Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.}, language = {en} } @article{WenningerStecherSchmidt2019, author = {Wenninger, Marc and Stecher, Dominik and Schmidt, Jochen}, title = {SVM-Based Segmentation of Home Appliance Energy Measurements}, series = {Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019}, journal = {Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019}, pages = {1666 -- 1670}, year = {2019}, abstract = {Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change. Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information. We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households.}, language = {en} }