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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(More)
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(More)
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(More)
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(More)
In this paper, we present a method for estimating the local keys of an audio signal. We propose to address the problem of local key finding by investigating the possible combination and extension of different previous proposed global key estimation approaches. The specificity of our approach is that we introduce key dependency on the harmonic and the metric(More)
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,(More)
We propose the use of Markov Logic Networks (MLNs) as a highly flexible and expressive formalism for the harmonic analysis of audio signals. Using MLNs information about the physical and semantic content of the signal can be intuitively and compactly encoded and expert knowledge can be easily expressed and combined using a single unified formal model that(More)
Robust Principal Component Analysis (RPCA) is a technique to decompose signals into sparse and low rank components, and has recently drawn the attention of the MIR field for the problem of separating leading vocals from accompaniment, with appealing results obtained on small excerpts of music. However, the performance of the method drops when processing(More)
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(More)