8,365 research outputs found
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
Humor is a unique and creative communicative behavior displayed during social
interactions. It is produced in a multimodal manner, through the usage of words
(text), gestures (vision) and prosodic cues (acoustic). Understanding humor
from these three modalities falls within boundaries of multimodal language; a
recent research trend in natural language processing that models natural
language as it happens in face-to-face communication. Although humor detection
is an established research area in NLP, in a multimodal context it is an
understudied area. This paper presents a diverse multimodal dataset, called
UR-FUNNY, to open the door to understanding multimodal language used in
expressing humor. The dataset and accompanying studies, present a framework in
multimodal humor detection for the natural language processing community.
UR-FUNNY is publicly available for research
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