Hélène Papadopoulos

Large-Scale Study of Chord Estimation Algorithms Based on Chroma Representation and HMM

This paper deals with the automatic estimation of chord progression over time of an audio file. From the audio signal, a set of chroma vectors representing the pitch content of the file over time is extracted. From these observations the chord progression is then estimated using hidden Markov models. Several methods are proposed that allow taking into account music theory, perception of key and presence of higher harmonics of pitch notes. The proposed methods are then compared to existing algorithms. A large-scale evaluation on 110 hand-labeled songs from the Beatles allows concluding on improvement over the state of the art.

Simultaneous Beat and Downbeat-Tracking Using a Probabilistic Framework: Theory and Large-Scale Evaluation

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.

Simultaneous estimation of chord progression and downbeats from an audio file

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.

Local Key Estimation From an Audio Signal Relying on Harmonic and Metrical Structures

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.

Exploiting structural relationships in audio music signals using Markov Logic Networks

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.

A structured nonnegative matrix factorization for source separation

In this paper, we propose a new unconstrained nonnegative matrix factorization method designed to utilize the multilayer structure of audio signals to improve the quality of the source separation. The tonal layer is sparse in frequency and temporally stable, while the transient layer is composed of short term broadband sounds. Our method has a part well suited for tonal extraction which decomposes the signals in sparse orthogonal components, while the transient part is represented by a regular nonnegative matrix factorization decomposition. Experiments on synthetic and real music data in a source separation context show that such decomposition is suitable for audio signal. Compared with three state-of-the-art harmonic/percussive decomposition algorithms, the proposed method shows competitive performances.

Models for Music Analysis From a Markov Logic Networks Perspective

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.

Content-adaptive speech enhancement by a sparsely-activated dictionary plus low rank decomposition

One powerful approach to speech enhancement employs strong models for both speech and noise, decomposing a mixture into the most likely combination. But if the noise encountered differs significantly from the systems assumptions, performance will suffer. In previous work, we proposed a speech enhancement model that decomposes the spectrogram into sparse activation of a dictionary of target speech templates, and a low-rank background model. This makes few assumptions about the noise, and gave appealing results on small excerpts of noisy speech. However, when processing whole conversations, the foreground speech may vary in its complexity and may be unevenly distributed throughout the recording, resulting in inaccurate decompositions for some segments. In this paper, we explore an adaptive formulation of our previous model that incorporates separate side information to guide the decomposition, making it able to better process entire conversations that may exhibit large variations in the speech content.

Sparse and structured decomposition of audio signals on hybrid dictionaries using musical priors

This paper investigates the use of musical priors for sparse expansion of audio signals of music, on an overcomplete dual-resolution dictionary taken from the union of two orthonormal bases that can describe both transient and tonal components of a music audio signal. More specifically, chord and metrical structure information are used to build a structured model that takes into account dependencies between coefficients of the decomposition, both for the tonal and for the transient layer. The denoising task application is used to provide a proof of concept of the proposed musical priors. Several configurations of the model are analyzed. Evaluation on monophonic and complex polyphonic excerpts of real music signals shows that the proposed approach provides results whose quality measured by the signal-to-noise ratio is competitive with state-of-the-art approaches, and more coherent with the semantic content of the signal. A detailed analysis of the model in terms of sparsity and in terms of interpretability of the representation is also provided and shows that the model is capable of giving a relevant and legible representation of Western tonal music audio signals.

Drum extraction in single channel audio signals using multi-layer Non negative Matrix Factor Deconvolution

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.