Themen
Auf dieser Seite finden Sie aktuelle Themen für Abschlussarbeiten (Bachelor, Master). Sollten Sie eigene Ideen für Themen im Umfeld der Forschungsthemen der Professur Computational Humanities haben, kontaktieren Sie uns gerne.
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Topic: Automatic String Tuning Estimation for Guitar Recordings
Description: Guitar performances involve multiple strings that can be tuned in various ways, creating challenges for automatic analysis and transcription. Since the same pitch can often be played at different fretboard positions, distinguishing between standard and open tunings, which are frequently used to simplify chord fingering, remains an open problem in music information retrieval (MIR). This thesis investigates methods for estimating the tuning configuration of string instruments directly from audio recordings, building upon existing datasets such as the IDMT Guitar Dataset and recordings by the Peruvian guitarist Daniel Kirwayo. The work may combine data-driven audio analysis and symbolic approaches to explore how tuning affects fingering effort and chord formation.
Keywords: tuning estimation, guitar analysis, transcription, music information retrieval (MIR)
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Topic: Automatic Guitar Tablature Generation
Description: Translating musical notes into guitar tablature is a challenging task due to the ambiguity between pitch and playing position—the same note can be produced on multiple strings and frets. Guitarists typically select fingerings that minimize finger stretching and movement across the fretboard, making tablature generation a complex optimization problem constrained by tuning, pitch range, and playability. This thesis investigates methods for automatically generating guitar tablature from symbolic music scores or outputs of automatic music transcription systems, aiming to produce realistic and ergonomic fingerings that reflect how musicians actually play.
Keywords: guitar tablature, automatic music transcription, fingering optimization, music information retrieval (MIR), performance modeling
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Topic: Linking Perception and Recognition — Psychoacoustic Cues in Urban Soundscapes
Description: How do people’s perceptual impressions of urban soundscapes relate to what machines detect in the same recordings? This thesis explores the relationship between psychoacoustic parameters (such as loudness, sharpness, roughness, and tonality) and sound event categories identified by state-of-the-art deep learning models (e.g., PANN, CLAP). Using publicly available datasets like TAU Urban Acoustic Scenes 2019 or SONYC Urban Sound Tagging (SONYC-UST), the work aims to reveal typical co-occurrence patterns between perceptual attributes, sound classes, and acoustic scenes to deepen our understanding of urban acoustic environments.
Keywords: soundscape, psychoacoustics, sound event detection, DCASE
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Topic: Soundscapes of Bamberg — Mapping Urban Acoustic Environments
Description: How does the sound of a city change throughout the day, and how do its residents perceive these variations? This thesis explores the urban soundscapes of Bamberg by combining geospatial data, citizen surveys, and immersive audio recordings. Based on an initial survey and geodata analysis, characteristic locations with distinct time-of-day sound compositions will be identified and recorded using binaural or ambisonic microphones. These soundscapes will then be made explorable in an interactive web interface, accompanied by visualizations of survey results that reflect the affective impact of different sounds and environments on residents.
Keywords: soundscape analysis, urban acoustics, geodata, visualization
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Topic: Automatic Detection of Pronunciation Errors in L2 English
This project will be co-supervised by Prof. Dr Julia Schlüter, Chair of English Linguistics at the University of Bamberg.
Description: This thesis will investigate how machine learning can automatically identify pronunciation errors made by advanced German learners of English. It is based on a novel dataset consisting of 250 one-minute recordings of a fixed diagnostic text. These recordings are annotated with 34 categories of typical learner errors and are accompanied by an IPA transcription. The thesis will involve aligning spectrogram representations of the recordings with the IPA reference text, extracting relevant acoustic–phonetic features and training a model to recognise mispronunciations such as final devoicing, voicing confusions, /v/–/w/ substitutions or vowel mergers. An alternative approach would be to investigate whether a speech recognition model such as Whisper could be fine-tuned for this task. The project also aims to evaluate the accuracy of detection across error types and analyse which acoustic cues most reliably distinguish correct from incorrect pronunciations. This will provide insights relevant to automatic pronunciation assessment.
Keywords: pronunciation error detection; L2 English; IPA alignment; acoustic modelling; speech analysis; Whisper