Holger Kirchhoff

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Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse(More)
Automatic music transcription is considered by many to be the Holy Grail in the field of music signal analysis. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse(More)
In this paper, we address the task of semi-automatic music transcription in which the user provides prior information about the polyphonic mixture under analysis. We propose a non-negative matrix deconvolution framework for this task that allows instruments to be represented by a different basis function for each fundamental frequency (“shift(More)
For a user-assisted music transcription system in which the user is asked to label some notes for each instrument in the recording, we investigate ways to limit the amount of information the user has to provide. Different methods are proposed and experimentally compared that enable the estimation of template spectra at pitch positions that have not been(More)
For the task of semi-automatic music transcription, we extended our framework for shift-variant non-negative matrix deconvolution (svNMD) to work with multiple templates per instrument and pitch. A k-means clustering based learning algorithm is proposed that infers the templates from the data based on the provided user information. We experimentally(More)
In this paper we study the relative phase offsets between partials in the sustained part of harmonic sounds and investigate their suitability for complex matrix decomposition of spectrograms. We formally introduce this property in a sinusoidal model and visualise the phase relations of a musical instrument. A model of complex matrix decomposition in the(More)
We present an algorithm for tracking individual instruments in polyphonic music recordings. The algorithm takes as input the instrument identities of the recording and uses non-negative matrix factorisation to compute an instrument-independent pitch activation function. The Viterbi algorithm is applied to find the most likely path through a number of(More)
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