Martín Haro

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. Here we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets(More)
Timbre is a key perceptual feature that allows discrimination between different sounds. Timbral sensations are highly dependent on the temporal evolution of the power spectrum of an audio signal. In order to quantitatively characterize such sensations, the shape of the power spectrum has to be encoded in a way that preserves certain physical and perceptual(More)
In this paper we present an approach towards the classification of pitched and unpitched instruments in polyphonic audio. In particular, the presented study accounts for three aspects currently lacking in literature: model scalability to polyphonic data, model generalisation in respect to the number of instruments, and incorporation of perceptual(More)
Recommending relevant and novel music to a user is one of the central applied problems in music information research. In the present work we propose three content-based approaches to this task. Starting from an explicit set of music tracks provided by the user as evidence of his/her music preferences, we infer high-level semantic descriptors, covering(More)
This paper analyses how audio features related to different musical facets can be useful for the comparative analysis and classification of music from diverse parts of the world. The music collection under study gathers around 6,000 pieces, including traditional music from different geographical zones and countries, as well as a varied set of Western(More)
We address here the automatic description of percussive events in real-world polyphonic music. By taking a pattern recognition approach we evaluate more than 2,450 objectlevel features. Three binary instrument-wise support vector machines (SVM) are built from a training set of more that 100 songs and 10 genres. Then, we use these binary models to build a(More)
Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user’s musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her(More)
The music we like (i.e. our musical preferences) encodes and communicates key information about ourselves. Depicting such preferences in a condensed and easily understandable way is very appealing, especially considering the current trends in social network communication. In this paper we propose a method to automatically generate, given a provided set of(More)
Sampling can be described as the reuse of a fragment of another artist’s recording in a new musical work. This project aims at developing an algorithm that, given a database of candidate recordings, can detect samples of these in a given query. The problem of sample identification as a music information retrieval task has not been addressed before, it is(More)
The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, contentbased preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates(More)