Raúl Santos-Rodriguez

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We present a new system for the harmonic analysis of popular musical audio. It is focused on chord estimation, although the proposed system additionally estimates the key sequence and bass notes. It is distinct from competing approaches in two main ways. First, it makes use of a new improved chromagram representation of audio that takes the human perception(More)
In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contributions from the last 14 years of research are summarized, with detailed discussions of the following topics: feature extraction, modeling strategies, model training and datasets, and evaluation strategies. Results from the annual benchmarking(More)
The most significant problem faced by Machine Learningbased chord recognition systems is arguably the lack of highquality training examples. In this paper, we address this problem by leveraging the availability of chord annotations from guitarist websites. We show that such annotations can be used as partial supervision of a semi-supervised chord(More)
We present a new system, Harmony progression (HP) analyzer, for simultaneous estimation of keys, chords, and bass notes from music audio. It makes use of a novel chromagram of audio that takes perception of loudness into account. Furthermore, it is fully based on machine learning (ML), such that it is potentially applicable to a wider range of genres as(More)
Detecting underlying trends in time series is important in many settings, such as market analysis (stocks, social media coverage) and system monitoring (production facilities, networks). Although many properties of the trends are common across different domains, others are domain-specific. In particular, modelling human activities such as their behaviour on(More)
To assess the performance of an automatic chord estimation system, reference annotations are indispensable. However, owing to the complexity of music and the sometimes ambiguous harmonic structure of polyphonic music, chord annotations are inherently subjective, and as a result any derived accuracy estimates will be subjective as well. In this paper, we(More)
Recently a large amount of new chord annotations have been made available. This raises hopes for further development in automatic chord estimation. While more data seems to imply better performance, a major challenge however, is the wide variety of genres covered by these new data. As a result, the genre-independent training scheme as is common today is(More)
Methods that find insightful low-dimensional projections are essential to effectively explore high-dimensional data. Principal Component Analysis is used pervasively to find low-dimensional projections, not only because it is straightforward to use, but it is also often effective, because the variance in data is often dominated by relevant structure.(More)
We present a new approach to evaluate chord recognition systems on songs which do not have full annotations. The principle is to use online chord databases to generate high accurate “pseudo annotations” for these songs and compute “pseudo accuracies” of test systems. Statistical models that model the relationship between “pseudo accuracy” and real(More)
Projection Pursuit aims to facilitate visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge in Projection Pursuit is the design of a projection index—a suitable quality measure to maximise. We introduce a strategy for tackling this problem based on quantifying the amount of information a(More)