In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm and bass content to feed convolutional neural networks that are adapted to take advantage of each feature characteristics. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the metrical continuity of a song. We then perform an evaluation of our system on a large base of 9 datasets, compare its performance to 4 other published algorithms and obtain a significant increase of 16.8 percent points compared to the second best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.
To take into account the slow bar length variation over time, induction methods are often used. The estimated downbeat sequence will be the one that maximizes the downbeat detection function and minimize the bar length variation. It can be done in a greedy way, one downbeat after another, starting at the first downbeat @cite_22 @cite_16 or at the start of the first characteristic rhythm pattern @cite_40 . To avoid being stuck in a local minimum, most algorithms search a more global downbeat sequence path. It can be done with dynamic programming @cite_38 or Hidden Markov Models (HMM) @cite_31 @cite_35 @cite_6 that can sometimes be coupled with a learned language model @cite_21 . A particularly interesting temporal model is the dynamic bar pointer model @cite_13 . It consists of a dynamic bayesian network jointly modeling the downbeat positions, the tempo and the rhythm pattern. This method was refined to improve the observation probabilities @cite_30 and reduce of the computational complexity of the inference @cite_52 . Most of the aforementioned systems don't handle varied time signatures during the downbeat sequence extraction.
@cite_30 @cite_38 @cite_35 @cite_22 @cite_21 @cite_52 @cite_6 @cite_40 @cite_31 @cite_16 @cite_13
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This paper deals with the simultaneous estimation of beat and downbeat location in an audio-file. We propose a probabilistic framework in which the time of the beats and their associated beat-position-inside-a-bar roles; hence, the downbeats, are considered as hidden states and are estimated simultaneously using signal observations. For this, we propose a “reverse” Viterbi algorithm which decodes hidden states over beat-numbers. A beat-template is used to derive the beat observation probabilities. For this task, we propose the use of a machine-learning method, the Linear Discriminant Analysis, to estimate the most discriminative beat-templates. We propose two functions to derive the beat-position-inside-a-bar observation probability: the variation over time of chroma vectors and the spectral balance. We then perform a large-scale evaluation of beat and downbeat-tracking using six test-sets. In this, we study the influence of the various parameters of our method, compare this method to our previous beat and downbeat-tracking algorithms, and compare our results to state-of-the-art results on two test-sets for which results have been published. We finally discuss the results obtained by our system in the MIREX-09 and MIREX-10 contests for which our system ranked among the first for the “McKinney Collection” test-set. This dissertation covers machine listening techniques for the automated real-time analysis of live drum performances. Onset detection, drum detection, beat tracking, and drum pattern analysis are combined into a system that provides rhythmic information useful in performance analysis, synchronization, and retrieval. The techniques are designed with real-time use in mind but can easily be adapted for offline batch use for large scale rhythm analysis. At the front end of the system, onset and drum detection provide the locations, types, and amplitudes of percussive events. The onset detector uses an adaptive, causal threshold in order to remain robust to large dynamic swings. For drum detection, a gamma mixture model is used to compute multiple spectral templates per drum onto which onset events can be decomposed using a technique based on non-negative matrix factorization. Unlike classification-based approaches to drum detection, this approach provides amplitude information which is invaluable in the analysis of rhythm. In addition, the decay of drum events are modeled using "tail" templates , which when used with multiple spectral templates per drum, reduce detection errors by 42 . The beat tracking component uses multiple period hypotheses and an ambiguity measure in order to choose a reliable pulse estimate. Results show that using multiple hypotheses significantly improves tracking accuracy compared to a single period model. The drum pattern analysis component uses the amplitudes of the detected drum onsets and the metric grid defined by the beat tracker as inputs to a generatively pre-trained deep neural network in order to estimate high-level rhythmic information. The network is tested with beat alignment tasks, including downbeat detection, and reduces alignment errors compared to a simple template correlation approach by up to 59 . We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval. A method is described which analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a new technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis. The method is validated using a manually annotated database of 474 music signals from various genres. The method works robustly for different types of music and improves over two state-of-the-art reference methods in simulations. This paper describes a Hidden Markov Model (HMM)-based method of automatic transcription of MIDI (Musical Instrument Digital Interface) signals of performed music. The problem is formulated as recognition of a given sequence of fluctuating note durations to find the most likely intended note sequence utilizing the modern continuous speech recognition technique. Combining a stochastic model of deviating note durations and a stochastic grammar representing possible sequences of notes, the maximum likelihood estimate of the note sequence is searched in terms of Viterbi algorithm. The same principle is successfully applied to a joint problem of bar line allocation, time measure recognition, and tempo estimation. Finally, durations of consecutive spl eta n notes are combined to form a "rhythm vector" representing tempo-free relative durations of the notes and treated in the same framework. Significant improvements compared with conventional "quantization" techniques are shown. 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. This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment, and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs, and that using detector-like models for each target drum provides a better performance than modelling drum combinations. This paper focuses on the automatic extraction of beat structure from a musical piece. A novel statistical approach to modeling beat sequences based on the application of Hidden Markov Models (HMM) is introduced. The resulting beat labels are obtained by running the Viterbi decoder and subsequent lattice rescoring. For the observation vectors we propose a new feature set that is based on the impulsive and harmonic components of the reassigned spectrogram. Different components of observation vectors have been investigated for their efficiency. The main advantage of the proposed approach is the absence of imposed deterministic rules. All the parameters are learned from the training data, and the experimental results show the efficiency of the proposed schema. We present a new technique for joint estimation of the chord progression and the downbeats from an audio file. Musical signals are highly structured in terms of harmony and rhythm. In this paper, we intend to show that integrating knowledge of mutual dependencies between chords and metric structure allows us to enhance the estimation of these musical attributes. For this, we propose a specific topology of hidden Markov models that enables modelling chord dependence on metric structure. This model allows us to consider pieces with complex metric structures such as beat addition, beat deletion or changes in the meter. The model is evaluated on a large set of popular music songs from the Beatles that present various metric structures. We compare a semi-automatic model in which the beat positions are annotated, with a fully automatic model in which a beat tracker is used as a front-end of the system. The results show that the downbeat positions of a music piece can be estimated in terms of its harmonic structure and that conversely the chord progression estimation benefits from considering the interaction between the metric and the harmonic structures. Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al [20], which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. For learning rhythmic patterns and evaluating beat and downbeat tracking, 697 ballroom dance pieces were annotated with beat and measure information. The results showed that explicitly modeling rhythmic patterns of dance styles drastically reduces octave errors (detection of half or double tempo) and substantially improves downbeat tracking. This paper proposes a Bayesian approach for automatic music transcription of polyphonic MIDI signals based on generative modeling of onset occurrences of musical notes. Automatic music transcription involves two subproblems that are interdependent of each other: rhythm recognition and tempo estimation. When we listen to music, we are able to recognize its rhythm and tempo (or beat location) fairly easily even though there is ambiguity in determining the individual note values and tempo. This may be made possible through our empirical knowledge about rhythm patterns and tempo variations that possibly occur in music. To automate the process of recognizing the rhythm and tempo of music, we propose modeling the generative process of a MIDI signal of polyphonic music by combining the sub-process by which a musically natural tempo curve is generated and the sub-process by which a set of note onset positions is generated based on a 2-dimensional rhythm tree structure representation of music, and develop a parameter inference algorithm for the proposed model. We show some of the transcription results obtained with the present method.