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Nonnegative matrix factorization (NMF) is used to derive a novel description for the timbre of musical sounds. Using NMF, a spectrogram is factorized providing a characteristic spectral basis. Assuming a set of spectrograms given a musical genre, the space spanned by the vectors of the obtained spectral bases is modeled statistically using mixtures of(More)
This paper introduces scale transforms to measure rhythmic similarity between two musical pieces. The rhythm of a piece of music is described by the scale transform magnitude, computed by transforming the sample autocorrelation of its onset strength signal to the scale domain. Then, two pieces can be compared without the impact of tempo differences by using(More)
In this paper, we suggest a novel group delay based method for the onset detection of pitched instruments. It is proposed to approach the problem of onset detection by examining three dimensions separately: phase (i.e., group delay), magnitude and pitch. The evaluation of the suggested onset detectors for phase, pitch and magnitude is performed using a new(More)
As a special case of the Mellin transform, the scale transform has been applied in various signal processing areas, in order to get a signal description that is invariant to scale changes. In this paper, the scale transform is applied to autocorrelation sequences derived from music signals. It is shown that two such sequences, when derived from similar(More)
In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method “selective sampling,” is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of(More)
The subject of this paper is the conversion of a given speaker's voice (the source speaker) into another identified voice (the target one). We assume we have at our disposal a large amount of speech samples from source and target voice with at least a part of them being parallel. The proposed system is built on a mapping function between source and target(More)
In this paper we establish a threshold for perceptually acceptable beat tracking based on the mutual agreement of a committee of beat trackers. In the first step we use an existing annotated dataset to show that mutual agreement can be used to select one committee member as the most reliable beat tracker for a song. Then we conduct a listening test using a(More)
In this paper, we approach the tasks of beat tracking, down-beat recognition and rhythmic style classification in non-Western music. Our approach is based on a Bayesian model, which infers tempo, downbeats and rhythmic style, from an audio signal. The model can be automatically adapted to rhythmic styles and time signatures. For evaluation , we compiled and(More)
In this paper, we propose a new state-of-the-art particle filter (PF) system to infer the metrical structure of musical audio signals. The new inference method is designed to overcome the problem of PFs in multi-modal probability distributions, which arise due to tempo and phase ambiguities in musical rhythm representations. We compare the new method with a(More)