Figure 0. A ChucK-based programming model for building audio analysis and synthesis programs. In this paper, we present a new programming model for performing audio analysis, spectral processing, and feature extraction in the ChucK programming language. The solution unifies analysis and synthesis in the same high-level, strongly-timed, and concurrent environment, extending and fully integrating with the existing language framework. In particular, we introduce the notion of a Unit Analyzer (UAna) and new constructs for dataflow, data types and semantics for operations in analysis domains, and mechanisms for seamlessly combining analysis and synthesis tasks in a precise, sample-synchronous manner. We present the motivation of our system, and describe new language-level syntaxes, semantics, and the underlying implementation. We provide code examples and discuss potential uses and benefits of the system for audio researchers, performers, and teachers. 1.

Reinforcement learning has great potential applicability in computer music, particularly for interactive scenarios and the production of effective control policies for systems. This paper considers in particular the case of interactive music systems, where a software agent can be trained online during a rehearsal session with a musician. A small-scale system is described which uses the Sarsa(λ) algorithm to update state-action values to determine a policy for output behaviours over a modest feature space. It is hoped that issues arising can be discussed fruitfully in a workshop context. 1.

Current research programmes in computer music may draw from developments in agent technology; music may provide an excellent test case for agent research. This paper describes the challenge of building agents for concert performance which allow close and rewarding interaction with human musicians. This is easier said than done; the fantastic abilities of human musicians in fluidity of action and cultural reference makes for a difficult mandate. The problem can be cast as that of building an autonomous agent for the (unforgiving) realtime musical environment. Live music is a challenging domain to model, with high dimensionality of descriptions and fast learning, responses and effective anticipation required. A novel symbolic interactive music system called Improvagent is presented as a framework for the testing of reinforcement learning over dynamic state-action case libraries, in a context of MIDI piano improvisation. Reinforcement signals are investigated based on the quality of musical prediction, and on the degree of influence in interaction. The former is found to be less effective than baseline methods of assumed stationarity and of simple nearest neighbour case selection. The latter holds more promise; an agent may be able to assess the value of an action in response to an observed state with respect to the potential for stability, or the promotion of change in future states, enabling controlled musical interaction. 1.

of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with the standard referencing practices of the discipline. I acknowledge the helpful guidance and support of my supervisor, Dr Mark Plumbley. In this thesis we investigate the automatic extraction of rhythmic and metrical information from audio signals. Primarily we address three analysis tasks: the extraction of beat times, equivalent to the human ability of foottapping in time to music; finding bar boundaries, which can be considered analogous to counting the beats of the bar; and thirdly the extraction of a predominant rhythmic pattern to characterise the distribution of note onsets within the bars. We extract beat times from an onset detection function using a twostate beat tracking model. The first state is used to discover an initial tempo and track tempo changes, while the second state maintains contextual continuity within consistent tempo hypotheses. The bar boundaries are recovered by finding the spectral difference between beat synchronous analysis frames, and the predominant rhythmic pattern by clustering bar length onset detection function frames. In addition to the new techniques presented for extracting rhythmic information, we also address the problem of evaluation, that of how to quantify the extent to which the analysis has been successful. To this aim we propose a new formulation for beat tracking evaluation, where accuracy is measured in terms of the entropy of a beat error histogram. To illustrate the combination of all three layers of analysis we present this research in the context of automatic musical accompaniment, such that the resulting rhythmic information can be realised as an automatic percussive accompaniment to a given input audio signal.

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Real-time classification applied to a vocal percussion signal holds potential as an interface for live musical control. In this article we propose a novel approach to resolving the tension between the needs for low-latency reaction and reliable classification, by deferring the final classification decision until after a response has been initiated. We introduce a new dataset of annotated human beatbox recordings, and use it to study the optimal delay for classification accuracy. We then investigate the effect of such delayed decision-making on the quality of the audio output of a typical reactive system, via a MUSHRA-type listening test. Our results show that the effect depends on the output audio type: for popular dance/pop drum sounds the acceptable delay is on the order of 12–35 ms. 1

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