Christian Dittmar

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The analysis and separation of audio signals into their original components is an important prerequisite to automatic transcription of music, extraction of metadata from audio data, and speaker separation in video conferencing. In this paper, a method for the separation of drum tracks from polyphonic music is proposed. It consists of an Independent(More)
This paper proposes a real-time capable method for transcribing and separating occurrences of single drum instruments in polyphonic drum recordings. Both the detection and the decomposition are based on Non-Negative Matrix Factorization and can be implemented with very small systemic delay. We propose a simple modification to the update rules that allows to(More)
In this paper we present a novel algorithm for automatic analysis, transcription, and parameter extraction from isolated polyphonic guitar recordings. In addition to general score-related information such as note onset, duration, and pitch, instrumentspecific information such as the plucked string, the applied plucking and expression styles are retrieved(More)
This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described:(More)
This publication presents a method for the automatic detection and classification of three distinct drum instruments in real world musical signals. The regarded instruments are kick, snare and hi-hat as agreed by the participants of the contest category Audio Drum Detection within the 2nd Annual Music Information Retrieval Evaluation eXchange (MIREX 2005).(More)
In this paper, we propose a novel approach for music similarity estimation. It combines temporal segmentation of music signals with source separation into so-called tone objects. We solely use the timbre-related audio features Mel-Frequency Cepstral Coefficients (MFCC) and Octave-based Spectral Contrast (OSC) to describe the extracted tone objects. First,(More)
In this paper, we propose a new method suitable for the automatic analysis of microtiming played by drummers in jazz recordings. Specifically, we aim to estimate the drummers’ swing ratio in excerpts of jazz recordings taken from the Weimar Jazz Database. A first approach is based on automatic detection of ride cymbal (RC) onsets and evaluation of relative(More)