Tue Lehn-Schiøler

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In large MP3 databases, files are typically generated with different parameter settings, i.e., bit rate and sampling rates. This is of concern for MIR applications, as encoding difference can potentially confound meta-data estimation and similarity evaluation. In this paper we will discuss the influence of MP3 coding for the Mel frequency cepstral(More)
We have collected a database of musical features from radio broadcasts and CD collections (N > 10 5). The database poses a number of hard modelling challenges including: Seg-mentation problems and missing and wrong meta-data. We describe our efforts towards cleaning the data using probability density estimation. We train conditional densities for checking(More)
The process of representing a large data set with a smaller number of vectors in the best possible way, also known as vector quantization, has been intensively studied in the recent years. Very efficient algorithms like the Kohonen self-organizing map (SOM) and the Linde Buzo Gray (LBG) algorithm have been devised. In this paper a physical approach to the(More)
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space'. The performance of the system is critically dependent on the number of(More)
Nowadays there is an increasing interest in developing methods for building music recommendation systems. In order to get a satisfactory performance from such a system, one needs to incorporate as much information about songs similarity as possible; however, how to do so is not obvious. In this paper, we build on the ideas of the Probabilistic Latent(More)
In this paper we propose to use an instantaneous ICA method (BLUES) to separate the instruments in a real music stereo recording. We combine two strong separation techniques to segregate instruments from a mixture: ICA and binary time-frequency masking. By combining the methods, we are able to make use of the fact that the sources are differently(More)
This demonstration illustrates how the methods developed in the MIR community can be used to provide real-time feedback to music users. By creating a genre classifier plug-in for a popular media player we present users with relevant information as they play their songs. The plug-in can furthermore be used as a data collection platform. After informed(More)