Conference proceeding
Multimodal Music and Lyrics Fusion Classifier for Artist Identification
2014 13th International Conference on Machine Learning and Applications, pp 506-509
Dec 2014
Abstract
Humans interact with each other using different communication modalities including speech, gestures and written documents. In the absence of one modality or presence of a noisy modality, other modalities can benefit precision of systems. HCI systems can also benefit from these multimodal communication models for different machine learning tasks. The provision of multiple modalities is motivated by usability, presence of noise in one modality and non-universality of a single modality. Combining multimodal information introduces new challenges to machine learning such as designing fusion classifiers. In this paper we explore the multimodal fusion of audio and lyrics for music artist identification. We compare our results with a single modality artist classifier and introduce new directions for designing a fusion classifier.
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Details
- Title
- Multimodal Music and Lyrics Fusion Classifier for Artist Identification
- Creators
- Kamelia Aryafar - Drexel UniversityAli Shokoufandeh - Drexel University
- Publication Details
- 2014 13th International Conference on Machine Learning and Applications, pp 506-509
- Conference
- 2014 13th International Conference on Machine Learning and Applications, 13th
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000380459000085
- Scopus ID
- 2-s2.0-84946688551
- Other Identifier
- 991019168814304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications