Download Model-based event labeling in the transcription of percussive audio signals In this paper we describe a method for the transcription of percussive audio signals which have been performed with arbitrary nondrum sounds. The system locates sound events from the input signal using an onset detector. Then a set of features is extracted from the onset times. Feature vectors are clustered and the clusters are assigned with labels which describe the rhythmic role of each event. For the labeling, a novel method is proposed which is based on metrical (temporal) positions of the sound events within the measures. The system is evaluated using monophonic percussive tracks consisting of non-drum sounds. In simulations, the system achieved a total error rate of 33.7%. Demo signals are available at URL:<http://www.cs.tut.fi/~paulus/demo/>.
Download Acoustic features for music piece structure analysis Automatic analysis of the structure of a music piece aims to recover its sectional form: segmentation to musical parts, such as chorus or verse, and detecting repeated occurrences. A music signal is here described with features that are assumed to deliver information about its structure: mel-frequency cepstral coefficients, chroma, and rhythmogram. The features can be focused on different time scales of the signal. Two distance measures are presented for comparing musical sections: “stripes” for detecting repeated feature sequences, and “blocks” for detecting homogenous sections. The features and their time scales are evaluated in a systemindependent manner. Based on the obtained information, the features and distance measures are evaluated in an automatic structure analysis system with a large music database with manually annotated structures. The evaluations show that in a realistic situation, feature combinations perform better than individual features.