4,575 research outputs found
Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation
This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements in the prediction rates are obtained for the valence dimension which is typically the most challenging core affect dimension for audio-based annotation. The specificity of genre categories is not crucial for the performance of ACTwg-SLPwg. The study also presents analytical insights into inferring a concise tag-based genre representation for genre-adaptive music mood analysis
Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations
We present the WASABI Song Corpus, a large corpus of songs enriched with
metadata extracted from music databases on the Web, and resulting from the
processing of song lyrics and from audio analysis. More specifically, given
that lyrics encode an important part of the semantics of a song, we focus here
on the description of the methods we proposed to extract relevant information
from the lyrics, such as their structure segmentation, their topics, the
explicitness of the lyrics content, the salient passages of a song and the
emotions conveyed. The creation of the resource is still ongoing: so far, the
corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at
different levels with the output of the above mentioned methods. Such corpus
labels and the provided methods can be exploited by music search engines and
music professionals (e.g. journalists, radio presenters) to better handle large
collections of lyrics, allowing an intelligent browsing, categorization and
segmentation recommendation of songs.Comment: 10 page
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