We describe how we used a data set of chorale harmonisations composed by Johann Sebastian Bach to train Hidden Markov Models. Using a probabilistic framework allows us to create a harmonisation system which learns from examples, and which can compose new harmonisations. We make a quantitative

This dissertation describes a chorale harmonisation system which uses Hidden Markov Models. We use a standard data set of chorale harmonisations composed by Johann Sebastian Bach. This data set provides a large number of stylistically similar harmonisations, and is freely available in a machine

We describe a computer program which is able to estimate the tempo and the times of musical beats in expressively performed music. The input data may be either digital audio or a symbolic representation of music such as MIDI. The data is processed off-line to detect the salient rhythmic events and the timing of these events is analysed to generate hypotheses of the tempo at various metrical levels. Based on these tempo hypotheses, a multiple hypothesis search nds the sequence of beat times which has the best fit to the rhythmic events. We show that estimating the perceptual salience of rhythmic events significantly improves the results. No prior knowledge of the tempo, meter or musical style is assumed; all required information is derived from the data. Results are presented for a range of different musical styles, including classical, jazz, and popular works with a variety of tempi and meters. The system calculates the tempo correctly in most cases, the most common error being a doubling or halving of the tempo. The calculation of beat times is also robust. When errors are made concerning the phase of the beat, the system recovers quickly to resume correct beat tracking, despite the fact that there is no high level musical knowledge encoded in the system.

1.1 Harmonisation............................ 1 1.1.1 Complexity of the chorales................. 1 2 Existing work on automatic harmonisation 3

Many authors have speculated about a close relationship between vocal expression of emotions and musical expression of emotions, but evidence bearing on this relationship has unfortunately been lacking. This review of 104 studies of vocal expression and 41 studies of music performance reveals similarities between the 2 channels concerning (a) the accuracy with which discrete emotions were communicated to listeners and (b) the emotion-specific patterns of acoustic cues used to communicate each emotion. The patterns are generally consistent with K. R. Scherer’s (1986) theoretical predictions. The results can explain why music is perceived as expressive of emotion, and they are consistent with an evolutionary perspective on vocal expression of emotions. Discussion focuses on theoretical accounts and directions for future research. Music: Breathing of statues. Perhaps: Stillness of pictures. You speech, where speeches end. You time, vertically poised on the courses of vanishing hearts.

The article analyzes the neural and functional grounding of language skills as well as their emergence in hominid evolution, hypothesizing stages leading from abilities known to exist in monkeys and apes and presumed to exist in our hominid ancestors right through to modern spoken and signed

in model based catchment and river basin management State-of-the-Art Report on Quality Assurance in modelling related to river basin management editor Jens Christian Refsgaard

Melody harmonisation is a centuries-old problem of long tradition, and a core aspect of composition in Western tonal music. In this work we describe FHARM, an automated system for melody harmonisation based on a functional model of harmony. Our system first generates multiple harmonically well

observations about the piece in question. The paradigmatic model proposed in this thesis for the identification of prominent melodic segments aims at contributing to the discourse between the theory and the analysis from a melodic point of view in a practical way. In the so-called similarity neighbourhood

This report is organized in the following way. In chapter 2, the review of the different melodic representations in the literature drives us to define different aspects of melodic description. In chapter 3, we describe the recurrent techniques used to extract this melodic description: from pitch