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Auflistung nach Autor:in "Schmitt, Maximilian"

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  • Textdokument
    Automatic Guitar String Detection by String-Inverse Frequency Estimation
    (INFORMATIK 2017, 2017) Geib, Tobias; Schmitt, Maximilian; Schuller, Björn
    In this work, we present a novel approach to approximating the fretboard position, i. e., the string and fret combination of guitar and bass recordings, using a feature we call String-Inverse Frequencies (SIFs). These frequencies are obtained from the opposite part of the string pressed down on a fretboard. We then show how they are calculated and proof their usefulness for guitar string detection. Additionally, a database is featured with recordings specifically tailored for this task. Furthermore, we demonstrate a basic approach using SIFs based on FFT spectral analysis and compare it to a basic standard classification process using Mel-Frequency Cepstral Coefficients and Support Vector Machines. The SIF-based approach showed a detection rate of up to F1 = 72% for both guitar and bass. Finally, we discuss further possibilities regarding SIFs.
  • Textdokument
    Recognising Guitar Effects - Which Acoustic Features Really Matter?
    (INFORMATIK 2017, 2017) Schmitt, Maximilian; Schuller, Björn
    The recognition of audio effects employed in recordings of electric guitar or bass has a wide range of applications in music information retrieval. It is meaningful in holistic automatic music transcription and annotation approaches for, e. g., music education, intelligent music search, or musicology. In this contribution, we investigate the relevance of a large variety of state-of-the-art acoustic features for the task of automatic guitar effect recognition. The usage of functionals, i. e., statistics such as moments and percentiles, is hereby compared to the bag-of-audio-words approach to obtain an acoustic representation of a recording on instance level. Our results are based on a database of more than 50 000 monophonic and polyphonic samples of electric guitars and bass guitars, processed with 10 different digital audio effects.