This paper presents a probabilistic model of temporal structure in music which allows joint inference of tempo, meter and rhythmic pattern. The framework of the model naturally quantifies these three musical concepts in terms of hidden state-variables, allowing resolution of otherwise apparent ambiguities in musical structure. At the heart of the system is a probabilistic model of a hypothetical ‘bar-pointer’ which maps an input signal to one cycle of a latent, periodic rhythmical pattern. The system flexibly accommodates different input signals via two observation models: a Poisson points model for use with MIDI onset data and a Gaussian process model for use with raw audio signals. The discrete state-space permits exact computation of posterior probability distributions for the quantities of interest. Results are presented for both observation models, demonstrating the ability of the system to correctly detect changes in rhythmic pattern and meter, whilst tracking tempo.

Abstract — Wireless networks introduce brand new opportunities for music delivery. The trend of using mobile devices in wireless networks, can significantly extent the recent change of paradigm in the model of music distribution, by allowing mobile clients to search for audio music in a network of wireless mobile hosts. This work, introduces the application of Content-Based Music Information Retrieval (CBMIR) in wireless ad-hoc networks. We investigate, for the first time in the literature, the challenges posed by the wireless medium and recognize the factors that require optimization. We propose novel techniques, which attain a significant reduction in both response time and network traffic, compared to naive approaches. Extensive experimental results illustrate the appropriateness, effectiveness and efficiency of the proposed method to this bandwidth-starving and volatile, due to mobility, environment. I.