Modalities in data: understanding text, images, and audio

Title

Modalities in data: understanding text, images, and audio

Creator

Adhegaonkar, Vikas; Goswami, Chinmoy; Thakur, Abhijeet; Varghese, Nikhil

Description

Data modalities, encompassing diverse forms such as text, audio, image, and video, play a pivotal role in shaping modern data analysis and machine learning applications. Each modality represents information in a unique format, requiring specific processing and interpretation methods. The integration of multiple modalities, known as multimodal data, enhances decision-making and predictive accuracy, particularly in complex systems like sentiment analysis, speech recognition, and medical diagnostics. Deep learning techniques have facilitated the seamless fusion of multimodal data, enabling a more comprehensive understanding across various fields, from healthcare to social media analytics. For example, combining text with images improves sentiment analysis, while integrating audio and video aids in more accurate speech recognition. However, the incorporation of multimodal data presents challenges, including data heterogeneity, synchronization issues, and dimensionality concerns. Data formats differ across modalities, and aligning them for cohesive analysis requires sophisticated algorithms and computational power. Despite these obstacles, multimodal data offers significant benefits, such as enhanced customer experience in business and increased diagnostic accuracy in health care. Furthermore, the rise of large datasets and artificial intelligence (AI) technologies has fueled innovation, enabling the development of more efficient models capable of uncovering intricate relationships within data. This chapter discusses various modalities, their applications, and the technological advancements driving their integration. It also highlights the challenges in multimodal data processing and the solutions being developed to address these complexities, offering valuable insights for businesses, researchers, and AI practitioners. 2026 Elsevier Inc. All rights reserved.

Source

Multimodal Learning Using Heterogeneous Data;pp.31-41

Date

01-01-2025

Publisher

Elsevier

Subject

audio; Data modality; image; multimodal learning; social media analytics; video

Coverage

Adhegaonkar V., School of Business and Management Balaji Institute of Technology & Management, Sri Balaji University, Pune, India; Goswami C., School of Business and Management Balaji Institute of Technology & Management, Sri Balaji University, Pune, India; Thakur A., School of Business and Management Balaji Institute of Technology & Management, Sri Balaji University, Pune, India; Varghese N., Department of Business Management, Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-044327528-9; 978-044327529-6;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-443-27528-9.00012-7

https://www.scopus.com/pages/publications/105032946735?origin=resultslist