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We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set(More)
The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them to construct machine learning models. Nevertheless, such tags are usually noisy and sparse. In this paper, we present a preliminary(More)
Studying the ways to recommend music to a user is a central task within the music information research community. From a content-based point of view, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. We propose two such distance measures. First, a low-level measure based on tempo-related(More)
—Measuring music similarity is essential for multi-media retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content. First, a low-level measure based on tempo-related description.(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)
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)
In this work we consider distance-based approaches to music recommendation, relying on an explicit set of music tracks provided by the user as evidence of his/her music preferences. Firstly, we propose a purely content-based approach , working on low-level (timbral, temporal, and tonal) and inferred high-level semantic descriptions of music. Secondly , we(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)
We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set(More)