Deep cross-modal learning has successfully demonstrated excellent performance in cross-
modal multimedia retrieval, with the aim of learning joint representations between different …

Music recommender systems have become an integral part of music streaming services
such as Spotify and Last. fm to assist users navigating the extensive music collections …

The striking ability of music to elicit emotions assures its prominent status in human culture
and every day life. Music is often enjoyed and sought for its ability to induce or convey …

We propose a multi-modal approach to the music emotion recognition (MER) problem,
combining information from distinct sources, namely audio, MIDI and lyrics. We introduce a …

This research proposes a framework for music mood classification that uses multiple and
complementary information sources, namely, music audio, lyric text, and social tags …

Integrating information about the listener's cultural background when building music
recommender systems has recently been identified as a means to improve recommendation …

This paper presents a new multimodal dataset Moodo that can aid the development of
affective music information retrieval systems. Moodo's main novelties are a multimodal …

We propose a novel approach to music emotion recognition by combining standard and
melodic features extracted directly from audio. To this end, a new audio dataset organized …

Melody and lyrics, reflecting two unique human cognitive abilities, are usually combined in
music to convey emotions. Although psychologists and computer scientists have made …

The task of retrieving audio content relevant to lyric queries and vice versa plays a critical
role in music-oriented applications. In this process, robust feature representations have to be …