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A supervised source separation method using non-negative matrix factorisation is proposed for transcribing mixtures of drum sounds. Despite the simple signal model, a high performance is obtained for signals without other instruments.

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Most of the drum transcription methods operate only on single-channel inputs, but multichannel signals are available in recording studios. A multichannel extension of the source separation method is proposed, and an increase in performance is observed in evaluations. Many of the drum transcription methods rely on detecting sound onsets for the segmentation of the signal. Detection errors will then decrease the overall performance of the system.


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To overcome this problem, a method utilising a network of connected hidden Markov models is proposed to perform the event segmentation and recognition jointly. The system is shown to be able to perform the transcription even from polyphonic music. The second main topic of this thesis is music structure analysis.

Two methods are proposed for this purpose.

Klapuri a and davy m eds signal processing methods

The first relies on defining a cost function for a description of the repeated parts. The second method defines a fitness function for descriptions covering the entire piece. The abstract cost and fitness functions are formulated in terms that can be determined from the input signal algorithmically, and optimisation problems are formulated. In both cases, an algorithm is proposed for solving the optimisation problems.

Robust Real-Time Music Transcription with a Compositional Hierarchical Model

The first method is evaluated on a small data set, and the relevance of the cost function terms is shown. This is to date the largest evaluation of a structure analysis method.

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The evaluations show that the proposed method outperforms a reference system on two of the data sets. Music structure analysis methods rarely provide musically meaningful names for the parts in the result.

An important idea related to this is the distinction between physical and perceptual sounds, as explained in Section Springer Professional. Back to the search result list.

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