Ametho is decribed which analyzes the basic patterno beats in a pieceo music, the musical meter. The analysis isperfoVRm jofoV at three different time scales: at the atopo tatum pulse level, at the tactus pulse level which com{CfixVm8 to thetempo o a piece, and at the musicalme0LN level.Aco9@9R signalsfro arbitrary musical genres arecojRV}}m8} Fo r the initial timefrequency analysis, a new technique ispro}Rx} which measures the degreeo musical accent as a functio o time atfo@ different frequency ranges. This isfoj{ wed by a banko cok filterreso}R@}R which extracts featuresfo estimating theperioj and phaseso the three pulses. The features arepro} essed by a proC}m8jfifi@fi moo which represents primitive musicalkno wledge and uses thelo w-level om@{j atio{ to perfoC jofo estimatio o the tatum, tactus, and measure pulses. Themom} takesinto accoj thetempojR dependencies between successive estimates and enablesbob causal and nom causal analysis. Themetho is validated using a manually annollym databaseo 474 music signals fro varioC genres. Themetho wo{j ro ustlyfo different typeso music andimpro veso ver two state-o8j9}@fimooofimo9@Cm9@VmoRmo Inde x TeFFD Aco9fim8{R@@fimooofimo9@Cm9@VmoRmo EDICS: 2-MUSI ToappeC in IEEE Trans. Spe0 h and Audio ProceLCY1 . 2004 IEEE. Pe rsonaluse of thismatefifiF ispeRfifiV0(V Howe ve ,peNfi10(VY to reNYNYY0 eNYNYY0 this mate0Dfi foradve1CC0(L or promotionalpurpose or for cre0YYR ne wcolle0(LC works for reNLR or r eR1fiL0( ution toseFNN s or lists, or to refiD anycopyrighte componeh of this work inothe works mustbe obtaine fromthe IEEE. I.

Signal processing methods for the automatic transcription of music are developed in this thesis. Music transcription is here understood as the process of analyzing a music signal so as to write down the parameters of the sounds that occur in it. The applied notation can be the traditional musical notation or any symbolic representation which gives sufficient information for performing the piece using the available musical instruments. Recovering the musical notation automatically for a given acoustic signal allows musicians to reproduce and modify the original performance. Another principal application is structured audio coding: a MIDI-like representation is extremely compact yet retains the identifiability and characteristics of a piece of music to an important degree. The scope of this thesis is in the automatic transcription of the harmonic and melodic parts of real-world music signals. Detecting or labeling the sounds of percussive instruments (drums) is not attempted, although the presence of these is allowed in the target signals. Algorithms are proposed that address two distinct subproblems of music transcription. The main part of the thesis is dedicated to multiple fundamental frequency (F0) estimation, that is, estimation of the F0s of several concurrent musical sounds. The other subproblem addressed is musical meter estimation. This has to do with rhythmic aspects of music and refers to the estimation of the regular pattern of strong and weak beats in a piece of music. For multiple-F0 estimation, two different algorithms are proposed. Both methods are based on an iterative approach, where the F0 of the most prominent sound is estimated, the sound is cancelled from the mixture, and the process is repeated for the residual. The first method is derived in a prag...

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The aim of this overview is to describe methods for the automatic transcription of Western polyphonic music. The transcription task is here understood as transforming an acoustic musical signal into a MIDI-like symbolic representation. Only pitched musical instruments are considered: recognizing the sounds of drum instruments is not discussed. The main emphasis is laid on estimating the multiple fundamental frequencies of several concurrent sounds. Various approaches to solve this problem are discussed, including methods that are based on modelling the human auditory periphery, methods that mimic the human auditory scene analysis function, signal model-based Bayesian inference methods, and data-adaptive methods. Another subproblem addressed is the rhythmic parsing of acoustic musical signals. From the transcription point of view, this amounts to the temporal segmentation of music signals at different time scales. The relationship between the two subproblems and the general structure of the transcription problem is discussed. 1.

The aim of this document is to discuss possible ways of describing some music constructs in a dual context. First, that of the current standard for multimedia content description: MPEG-7. Second, that of a specific so,are application, the Sound Palette (a tool for content-based management, content edition and transformation of simple audio phrases). We discuss some MPEG-7 limitations regarding different musical layers: melodic (present but underdeveloped), rhythmic (practically absent) and instrumental (present though using an exclusive procedure). Some proposals for overcoming them are presented in the context of our application.

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The thesis proposes an algorithm for the recognition of musical meter from acoustic signals of music. Musical meter is a part of rhythm that is constantly present in music, as it spans the musical time base. The proposed model is capable of finding metrical levels, including the beat and the tatum, in real time from a musical audio signal. The model comprises four main components: an onset detector, a tatum estimator, a phenomenal accent model, and a beat estimator. The onset detector finds distinct sound onsets from an acoustic signal, using multiband signal processing. After this, the tatum, which is the lowest metrical level, is computed from onset times. Phenomenal accents are computed from a set of 16 acoustic signal features using Bayesian pattern recognition. The tatum and the accents then yield the beat. The proposed model operates causally and is able to respond to tempo changes. The design of the model aims at generality in regard to musical genres, and thus the model is trained and tested using 330 music excerpts from multiple genres. The model performance varies according to the rhythmic difficulty of the input signal. Most pop/rock music poses no problems for the algorithm, while classical music and expressive jazz pieces are intractable. The model produces more errors than Eric Scheirer's beat tracker, but at the same time it follows more metrical levels than Scheirer's model. The results of this thesis are directly applicable in music production and post-processing. The access to musical time enables new levels of productivity and automation in both music software and hardware. Meter-synchronized comparison, mixing, and editing of pieces of music is possible. Robust meter recognition is a vital component of music information retrieval applications.

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

This paper presents and compares two methods of tracking the beat in musical performances, one based on a Bayesian decision framework and the other a gradient strategy. The techniques can be applied directly to a digitized performance (i.e., a soundfile) and do not require a musical score or a MIDI transcription. In both cases, the raw audio is first processed into a collection of “rhythm tracks” which represent the time evolution of various low-level features. The Bayesian approach chooses a set of parameters that represent the beat by modeling the rhythm tracks as a concatenation of random variables with a patterned structure of variances. The output of the estimator is a trio of parameters that represent the interval between beats, its change (derivative), and the position of the starting beat. Recursive (and potentially real time) approximations to the method are derived using particle filters, and their behavior is investigated via simulation on a variety of musical sources. The simpler method, which performs a gradient descent over a window of beats, tends to converge more slowly and to undulate about the desired answer. Several examples are presented that highlight both the strengths and weaknesses of the approaches.

This paper presents a beat tracking algorithm for musical audio signals. The method firstly extracts musical changepoints from the help signal and then uses a particle filtering algorithm to associate these to a tempo process. Results are comparable with the current state of the art.