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Abstract—We propose a new model called a Pairwise Markov Chain (PMC), which generalizes the classical Hidden Markov Chain (HMC) model. The generalization, which allows one to model more complex situations, in particular implies that in PMC the hidden process is not necessarily a Markov process. However, PMC allows one to use the classical Bayesian restoration methods like Maximum A Posteriori (MAP), or Maximal Posterior Mode (MPM). So, akin to HMC, PMC allows one to restore hidden stochastic processes, with numerous applications to signal and image processing, such as speech recognition, image segmentation, and symbol detection or classification, among others. Furthermore, we propose an original method of parameter estimation, which generalizes the classical Iterative Conditional Estimation (ICE) valid for of classical hidden Markov chain model, and whose extension to possibly non-Gaussian and correlated noise is briefly treated. Some preliminary experiments validate the interest of the new model. Index Terms—Bayesian restoration, hidden data, image segmentation, iterative conditional estimation, hidden Markov chain, pairwise Markov chain, unsupervised classification. 1

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Music alignment links events in a score and points on the audio performance time axis. All the parts of a recording can be thus indexed according to score information.

Abstract—Electronic Music Distribution is in need of robust and automatically extracted music descriptors. An important attribute of a piece of polyphonic music is what is commonly referred to as “the way it sounds”. While there has been a large quantity of research done to model the timbre of individual instruments, little work has been done to analyze “real world ” timbre mixtures such as the ones found in popular music. In this paper, we present our research about such “polyphonic timbres”. We describe an effective way to model the textures found in a given music signal, and show that such timbre models provide new solutions to many issues traditionally encountered in music signal processing and music information retrieval. Notably, we describe their applications for music similarity, segmentation and pattern induction. Index Terms—Feature extraction, information retrieval, multimedia database, music, pattern recognition.

We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. The sung theme (query) is converted into a sequence of pitch-intervals and rhythms. This sequence is compared to musical themes (targets) stored in a database. The top pieces are returned to the user in order of similarity to the sung theme. We describe, in detail, two different approaches to measuring similarity between database themes and the sung query. In the first, queries are compared to database themes using standard string-alignment algorithms. Here, similarity between target and query is determined by edit cost. In the second approach, pieces in the database are represented as hidden Markov models (HMMs). In this approach, the query is treated as an observation sequence and a target is judged similar to the query if its HMM has a high likelihood of generating the query. In this article we report our approach to the construction of a target database of themes, encoding, and transcription of user queries, and the results of preliminary experimentation with a set of sung queries. Our experiments show that while no approach is clearly superior to the other system, string matching has a slight advantage. Moreover, neither approach surpasses human performance.

Music alignment is the association of events in a score with points in the time axis of an audio signal. The signal is thus segmented according to the events in the score. We propose a new methodology for automatic alignment based on dynamic time warping, where the spectral peak structure is used to compute the local distance, enhanced by a model of attacks and of silence. The methodology can cope with performances considered difficult to align, like polyphonic music, trills, fast sequences, or multi-instrument music. An optimisation of the representation of the alignment path makes the method applicable to long sound files, so that unit databases can be fully automatically segmented and labeled. On 708 sequences of synthesised music, we achieved an average offset of 25 ms and an error rate of 2.5%.

A methodology is presented that allows a computer to play the role of musical accompanist in a non-improvised musical composition for soloist and accompaniment. The modeling of the accompaniment incorporates a number of distinct knowledge sources including timing information extracted in real-time from the soloist's acoustic signal, an understanding of the soloist's interpretation learned from rehearsals, and prior knowledge that guides the accompaniment toward musically plausible renditions. The solo and accompaniment parts are represented collectively as a large number of Gaussian random variables with a specified conditional independence structure --- a Bayesian Belief Network. Within this framework a principled and computationally feasible method for generating real-time accompaniment is presented that incorporates the relevant knowledge sources. The EM algorithm is used to adapt the accompaniment to the soloist's interpretation through a series of rehearsals. A demonstration is provided from J.S. Bach's Cantata 12.

We present a technique that, given a sequence of musical note onset times, performs simultaneous identification of the norated rhythm and the variable tempo associated with the times. Our formulation is probabilistic: We develop a stochastic model for the interconnected evolution of a rhythm process, a tempo process, and an observable process. This model allows the globally optimal identification of the most likely rhythm and tempo sequence, given the observed onset times. We demonstrate applications to a sequence of times derived from a sampled audio file and to MIDI data.

Score following is the synchronisation of a computer with a performer playing aknownmusicalscore.Itnowhasahistory of about twenty years as a research and musical topic, and is an ongoing project at Ircam. We present an overview of existing and historical score following systems, followed by fundamental definitions and terminology, and considerations about score formats, evaluation of score followers, and training. The score follower that we developed at Ircam is based on a Hidden Markov Model and on the modeling of the expected signal received from the performer. The model has been implemented in an audio and a Midi version, and is now being used in production. We report here our first experiences and our first steps towards a complete evaluation of system performances. Finally, we indicate directions how score following can go beyondtheartisticapplications known today.