Mohamed Sordo

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This paper presents findings about mood representations. We aim to analyze how do people tag music by mood, to create representations based on this data and to study the agreement between experts and a large community. For this purpose, we create a semantic mood space from last.fm tags using Latent Semantic Analysis. With an unsupervised clustering(More)
Music perception is highly intertwined with both emotions and context. Not surprisingly, many of the users’ information seeking actions aim at retrieving music songs based on these perceptual dimensions – moods and themes, expressing how people feel about music or which situations they associate it with. In order to successfully support music retrieval(More)
Music listening patterns can be influenced by contextual factors such as the activity a listener is involved in, the place one is located or physiological constants. As a consequence, musical listening choices might show some recurrent temporal patterns. Here we address the hypothesis that for some listeners, the selection of artists and genres could show a(More)
In this paper we present a way to annotate music collections by exploiting audio similarity. Similarity is used to propose labels (tags) to yet unlabeled songs, based on the content–based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to(More)
This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level.(More)
This paper presents an in–depth study of the social tagging mechanisms used in Freesound.org, an online community where users share and browse audio files by means of tags and content–based audio similarity search. We performed two analyses of the sound collection. The first one is related with how the users tag the sounds, and we could detect some(More)
The Bag-of-Frames (BoF) approach has been widely used in music genre classification. In this approach, music genres are represented by statistical models of low-level features computed on short frames (e.g. in the tenth of ms) of audio signal. In the design of such models, a common procedure in BoF approaches is to represent each music genre by sets of(More)
The task of identifying cover songs has formerly been studied in terms of a prototypical query retrieval framework. However, this framework is not the only one the task allows. In this article, we revise the task of identifying cover songs to include the notion of sets (or groups) of covers. In particular, we study the application of unsupervised clustering(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)
In this paper we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real time on a commercial music web site, specialised in content from the very long tail of music content. We compare it against two stand-alone recommenders, the first system based on usage and the(More)