One of the most general models of music signals considers that such signals can be represented as a sum of two distinct components: a tonal part that is sparse in frequency and temporally stable and a transient (or percussive) part that is composed of short-term broadband sounds. In this paper, we propose a novel hybrid method built upon nonnegative matrix factorization (NMF) that decomposes the time frequency representation of an audio signal into such two components. The tonal part is estimated by a sparse and orthogonal nonnegative decomposition, and the transient part is estimated by a straightforward NMF decomposition constrained by a pre-learned dictionary of smooth spectra. The optimization problem at the heart of our method remains simple with very few hyperparameters and can be solved thanks to simple multiplicative update rules. The extensive benchmark on a large and varied music database against four state of the art harmonic/percussive source separation algorithms demonstrate the merit of the proposed approach.

In this paper, we propose a supervised multilayer factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative matrix factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods.

Analyzing and formalizing the intricate mechanisms of music is a very challenging goal for Artificial Intelligence. Dealing with real audio recordings requires the ability to handle both uncertainty and complex relational structure at multiple levels of representation. Until now, these two aspects have been generally treated separately, probability being the standard way to represent uncertainty in knowledge, while logical representation being the standard way to represent knowledge and complex relational information. Several approaches attempting a unification of logic and probability have recently been proposed. In particular, Markov logic networks (MLNs), which combine first-order logic and probabilistic graphical models, have attracted increasing attention in recent years in many domains. This paper introduces MLNs as a highly flexible and expressive formalism for the analysis of music that encompasses most of the commonly used probabilistic and logic-based models. We first review and discuss existing approaches for music analysis. We then introduce MLNs in the context of music signal processing by providing a deep understanding of how they specifically relate to traditional models, specifically hidden Markov models and conditional random fields. We then present a detailed application of MLNs for tonal harmony music analysis that illustrates the potential of this framework for music processing.

We propose an innovative approach for music description at several time-scales in a single unified formalism. More specifically, chord information at the analysis-frame level and global semantic structure are integrated in an elegant and flexible model. Using Markov Logic Networks (MLNs) low-level signal features are encoded with high-level information expressed by logical rules, without the need of a transcription step. Our results demonstrate the potential of MLNs for music analysis as they can express both structured relational knowledge through logic as well as uncertainty through probabilities.

In this paper, we present a method for estimating the progression of musical key from an audio signal. We address the problem of local key finding by investigating the possible combination and extension of different previously proposed approaches for global key estimation. In this work, key progression is estimated from the chord progression. Specifically, we introduce key dependency on the harmonic and the metrical structures. A contribution of our work is that we address the problem of finding an analysis window length for local key estimation that is adapted to the intrinsic music content of the analyzed piece by introducing information related to the metrical structure in our model. Key estimation is not performed on empirically chosen segments but on segments that are expressed in relationship with the tempo period. We evaluate and analyze our results on two databases of different styles. We systematically analyze the influence of various parameters to determine factors important to our model, we study the relationships between the various musical attributes that are taken into account in our work, and we provide case study examples.

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.

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.

Harmony and metrical structure are some of the most important attributes of Western tonal music. In this paper, we present a new method for simultaneously estimating the chord progression and the downbeats from an audio file. For this, we propose a specific topology of hidden Markov models that allows us to model chords dependency on metrical structure. The model is evaluated on a dataset of 66 popular music songs from the Beatles and shows improvement over the state of the art.