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In algorithmic music composition, a simple technique involves selecting notes sequentially according to a transition table that specifies the probability of the next note as a function of the previous context. I describe an extension of this transition table approach using a recurrent autopredictive connectionist network called CONCERT. CONCERT is trained on a set of pieces with the aim of extracting stylistic regularities. CONCERT can then be used to compose new pieces. A central ingredient of CONCERT is the incorporation of psychologically-grounded representations of pitch, duration, and harmonic structure. CONCERT was tested on sets of examples artificially generated according to simple rules and was shown to learn the underlying structure, even where other approaches failed. In larger experiments, CONCERT was trained on sets of J. S. Bach pieces and traditional European folk melodies and was then allowed to compose novel melodies. Although the compositions are occasionally pleasa...

Many human behaviors re ect the attunement of our perceptual systems to rhythmic patterns of stimulation. Examples include dancing to music, speech communication, and the performance of a symphony orchestra. However, developing a computational model of rhythm perception has proven to be di cult for two main reasons. First, rhythm is holistic, yet rhythmic patterns evolve over time. Second, periodicities in rhythmic patterns typically exhibit variability in their timing. Many previous approaches to rhythm perception have ignored these two problems by abstracting time to the level of musical notation, and thus failed to address the fundamental issue of the perception of time. The approach taken in this thesis is that the developmentof a model of rhythm perception must rst address the perception of the time intervals which comprise rhythmic patterns. I propose a class of adaptive-oscillator processing units which track periodicities in rhythmic patterns (beats). Modest random variations in the timing of rhythmic patterns do not reduce the adaptive oscillator's ability to attain synchrony, and can even improve it. An Entrainment Model of human time perception is then developed. The

At least four elements characterize musical rhythm: 1) metric content, a quantized attribute � 2) ametric phrases, those unrelated to any tactus � 3) tempo variation, change in the number of beats per second � and 4) event shifting, time deviations from a steady beat. This deconstruction provides a means to represent timing features from di erent musical styles. Both tempo variation and event shift information can operate at di erent levels of the metric hierarchy found in music. This representation will facilitate the analysis and production of musical style to the extent thatitisrhythmically expressed. 1.

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This thesis describes an approach to representing musical rhythm in computational terms. The purpose of such an approach is to provide better models of musical time for machine accompaniment of human musicians and in that attempt, to better understand the processes behind human perception and performance. The intersections between musicology and artificial intelligence (AI) are reviewed, describing the rewards from the interdisciplinary study of music with AI techniques, and the converse benefits to AI research. The arguments for formalisation of musicological theories using AI and cognitive science concepts are presented. These bear upon the approach of research, considering ethnographic and process models of music versus traditionally descriptive methods of music study. This enquiry investigates the degree to which the human task of music can be studied and modelled computationally. It simultaneously performs the AI task of problem domain identification and constraint. The psycholo...

This paper is concerned with the handling of rhythm in music content processing applications. Keeping this framework in mind, we briefly report on terminology issues and the interdisciplinary nature of rhythm investigations, we then review approaches to computational modeling of rhythm and rhythm representation schemes. We comment the bottomup and top-down oriented approaches to computational modeling and the parallel that some authors make with physiological and cognitive views on rhythm perception. We argue that investigations should be listener-oriented, signal-oriented and application-oriented. 1

The use of linear phase Gabor transform wavelets is demonstrated as a robust analysis technique capable of making explicit many elements of human rhythm perception behaviour. Transforms over a continuous time-frequency plane (the scalogram) spanning rhythmic frequencies (0.1 to 100Hz) capture the multiple periodicities implied by beats at different temporal relationships. Wavelets represent well the transient nature of these rhythmic frequencies in performed music, in particular those implied by agogic accent, and at longer time-scales, by rubato. The use of the scalogram phase information provides a new approach to the analysis of rhythm. Measures of phase congruence over a range of frequencies are shown to be useful in highlighting transient rhythms and temporal accents. The performance of the wavelet transform is demonstrated on examples of performed monophonic percussive rhythms possessing intensity accents and rubato. The transform results indicate the location of such accents and...

We have developed a representation for musical rhythm and rhythmic structure based on concepts derived from African and African-American musics. Included in the representation are a model for expressive timing against an isochronous pulse, and a cellular approach to musical organization. In our implementation, the representation and its data structures are controlled and modified in real time using MAX. The richness of control over many meaningful musical quantities distinguishes our representation from those in more common usage, such as music notation programs, sequencers, and drum machines. 1 Introduction Many authors [2][4][7][8] have attempted to address the universal issue of rhythm perception and cognition. While these efforts feature rigorous approaches to data analysis and modeling, such work often contains musical assumptions specific to Western European classical music, even if the music under study lies outside of that tradition. It is clear that one cannot transcend one'...

The Normalized Positive (NPOS) model is a novel matching model that predicts downbeat location and pattern complexity in rhythmical patterns. Though similar models report success, the NPOS model is particularly effective at making these predictions while at the same time being theoretically and mathematically simple. In this paper, the details of the model are explored and a comparison is made to existing models. Several datasets are used to examine the complexity predictions of the model. Special attention is paid to the model's ability to account for the effects of musical experience on rhythm perception (McAuley & Semple, 1999; Drake, 1993).