In distributed microphone arrays, each microphone is connected to a separate recording device, which requires compensation for their different clock shifts. In this paper, we propose a new type of distributed arrays, in which the microphones are moving. This mobility introduces innovation that can be used to estimate the positions of both the sound sources and the microphones, as well as compensate for the clock shifts. Estimation is done using extended Kaiman filters with heuristics that improve the convergence. We discuss the proposed method in the context of mobile robotics: each microphone is attached to a controllable mobile robot. In this scenario, the array can be used to localize sound-emitting objects in the robot's environment, while providing information about robots' positions. Results show that the error of estimating robots' positions is as low as 5 cm with the source source localization error of 6 mm and the clock synchronization error of 178 μs, for the case of 9 microphones and 5 sound sources.

In this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor process priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bigram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of n-grams with a topic model, while smoothing them with the state-of-the-art method. Our model is evaluated by measuring its perplexity on a dataset of musical genre and harmony annotations 3 Genre Database (3GDB) and by measuring its ability to predict musical genre from chord sequences. In terms of perplexity, for a 262-chord dictionary we achieve a value of 2.74, compared to 18.05 for trigrams and 7.73 for a unigram topic model. In terms of genre prediction accuracy with 9 genres, the proposed approach performs about 33% better in relative terms than genre-dependent n-grams, achieving 60.4% of accuracy.

Symbolic pitch modeling is a way of incorporating knowledge about relations between pitches into the process of analyzing musical information or signals. In this paper, we propose a family of probabilistic symbolic polyphonic pitch models, which account for both the “horizontal” and the “vertical” pitch structure. These models are formulated as linear or log-linear interpolations of up to five sub-models, each of which is responsible for modeling a different type of relation. The ability of the models to predict symbolic pitch data is evaluated in terms of their cross-entropy, and of a newly proposed “contextual cross-entropy” measure. Their performance is then measured on synthesized polyphonic audio signals in terms of the accuracy of multiple pitch estimation in combination with a Nonnegative Matrix Factorization-based acoustic model. In both experiments, the log-linear combination of at least one “vertical” (e.g., harmony) and one “horizontal” (e.g., note duration) sub-model outperformed a pitch-dependent Bernoulli prior by more than 60% in relative cross-entropy and 3% in absolute multiple pitch estimation accuracy. This work provides a proof of concept of the usefulness of model interpolation, which may be used for improved symbolic modeling of other aspects of music in the future.

Multipitch estimation techniques are widely used for music transcription and acquisition of musical data from digital signals. In this paper, we propose a flexible harmonic temporal timbre model to decompose the spectral energy of the signal in the time-frequency domain into individual pitched notes. Each note is modeled with a 2-dimensional Gaussian mixture. Unlike previous approaches, the proposed model is able to represent not only the harmonic partials but also the inharmonic attack of each note. We derive an Expectation-Maximization (EM) algorithm to estimate the parameters of this model and illustrate the higher performance of the proposed algorithm than NMF algorithm and HTC algorithm for the task of multipitch estimation over synthetic and real-world data.