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Note onset detection and localization is useful in a number of analysis and indexing techniques for musical signals. The usual way to detect onsets is to look for "transient" regions in the signal, a notion that leads to many definitions: a sudden burst of energy, a change in the short-time spectrum of the signal or in the statistical properties, etc. The(More)
We describe a method of segmenting musical audio into structural sections based on a hierarchical labeling of spectral features. Frames of audio are first labeled as belonging to one of a number of discrete states using a hidden Markov model trained on the features. Histograms of neighboring frames are then clustered into segment-types representing distinct(More)
In this paper we propose a text represention for musical chord symbols that is simple and intuitive for musically trained individuals to write and understand, yet highly structured and unambiguous to parse with computer programs. When designing feature extraction algorithms, it is important to have a hand annotated test set providing a ground truth to(More)
In this paper, we describe current efforts towards interlink-ing music-related datasets on the Web. We first explain some initial interlinking experiences, and the poor results obtained by taking a na¨ıve approach. We then detail a particular interlinking algorithm, taking into account both the similarities of web resources and of their neighbours. We(More)
We present a study on the combined use of energy and phase information for the detection of onsets in musical signals. The resulting method improves upon both energy-based and phase-based approaches. The detection function, generated from the analysis of the signal in the complex frequency domain is sharp at the position of onsets and smooth everywhere(More)
We present a novel method for onset detection in musical signals. It improves over previous energy-based and phase-based approaches by combining both types of information in the complex domain. It generates a detection function that is sharp at the position of onsets and smooth everywhere else. Results on a hand-labelled data-set show that high detection(More)
Sonic Visualiser is the name for an implementation of a system to assist study and comprehension of the contents of audio data, particularly of musical recordings. It is a C++ application with a Qt4 GUI that runs on Windows, Mac, and Linux. It embodies a number of concepts which are intended to improve interaction with audio data and features, most notably(More)