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We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a(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)
In this paper we evaluate two methods for key estimation from polyphonic audio recordings. Our goal is to compare between a strategy using a cognition-inspired model and several machine learning techniques to find a model for tonality (mode and key note) determination of polyphonic music from audio files. Both approaches have as an input a vector of values(More)
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 unsuper-vised clustering(More)
This study investigates neural correlates of music-evoked fear and joy with fMRI. Studies on neural correlates of music-evoked fear are scant, and there are only a few studies on neural correlates of joy in general. Eighteen individuals listened to excerpts of fear-evoking, joy-evoking, as well as neutral music and rated their own emotional state in terms(More)
In this paper we present a study on music mood classification using audio and lyrics information. The mood of a song is expressed by means of musical features but a relevant part also seems to be conveyed by the lyrics. We evaluate each factor independently and explore the possibility to combine both, using natural language processing and music information(More)
This paper tackles the problem of feature aggregation for recognition of auditory scenes in unlabeled audio. We describe a new set of descriptors based on Recurrence Quantification Analysis (RQA), which can be extracted from the similarity matrix of a time series of audio descriptors. We analyze their usefulness for environmental audio recognition combined(More)