Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with

Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with a comprehensive introduction to the essential aspects of MER—including background, key techniques, and applications. This ground-breaking reference examines emotion from a dimensional perspective. It defines emotions in music as points in a 2D plane in terms of two of the most fundamental emotion dimensions according to psychologists—valence and arousal. The authors present a computational framework that generalizes emotion recognition from the categorical domain to real-valued 2D space. They also: Introduce novel emotion-based music retrieval and organization methods Describe a ranking-base emotion annotation and model training method Present methods that integrate information extracted from lyrics, chord sequence, and genre metadata for improved accuracy Consider an emotion-based music retrieval system that is particularly useful for mobile devices The book details techniques for addressing the issues related to: the ambiguity and granularity of emotion description, heavy cognitive load of emotion annotation, subjectivity of emotion perception, and the semantic gap between low-level audio signal and high-level emotion perception. Complete with more than 360 useful references, 12 example MATLAB® codes, and a listing of key abbreviations and acronyms, this cutting-edge guide supplies the technical understanding and tools needed to develop your own automatic MER system based on the automatic recognition model.

Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with a comprehensive introduction to the essential aspects of MER—including background, key techniques, and applications. This ground-breaking reference examines emotion from a dimensional perspective. It defines emotions in music as points in a 2D plane in terms of two of the most fundamental emotion dimensions according to psychologists—valence and arousal. The authors present a computational framework that generalizes emotion recognition from the categorical domain to real-valued 2D space. They also: Introduce novel emotion-based music retrieval and organization methods Describe a ranking-base emotion annotation and model training method Present methods that integrate information extracted from lyrics, chord sequence, and genre metadata for improved accuracy Consider an emotion-based music retrieval system that is particularly useful for mobile devices The book details techniques for addressing the issues related to: the ambiguity and granularity of emotion description, heavy cognitive load of emotion annotation, subjectivity of emotion perception, and the semantic gap between low-level audio signal and high-level emotion perception. Complete with more than 360 useful references, 12 example MATLAB® codes, and a listing of key abbreviations and acronyms, this cutting-edge guide supplies the technical understanding and tools needed to develop your own automatic MER system based on the automatic recognition model.

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians.

The problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a system that automatically analyzes the structures of a music file and annotates the file with the perceived emotions. Further, it explores emotion detection in MIDI and audio files. In the experiments presented here, the categorical and dimensional approaches were used, and the knowledge and expertise of music experts with a university music education were used for music file annotation. The automatic emotion detection systems constructed and described in the book make it possible to index and subsequently search through music databases according to emotion. In turn, the emotion maps of musical compositions provide valuable new insights into the distribution of emotions in music and can be used to compare that distribution in different compositions, or to conduct emotional comparisons of different interpretations of the same composition.

This book presents high-quality, original contributions (both theoretical and experimental) on Information Security, Machine Learning, Data Mining and Internet of Things (IoT). It gathers papers presented at ICETIT 2019, the 1st International Conference on Emerging Trends in Information Technology, which was held in Delhi, India, in June 2019. This conference series represents a targeted response to the growing need for research that reports on and assesses the practical implications of IoT and network technologies, AI and machine learning, data analytics and cloud computing, security and privacy, and next generation computing technologies.

This book constitutes the proceedings of the 25th International Symposium on Foundations of Intelligent Systems, ISMIS 2020, held in Graz, Austria, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 35 full and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. Included is also one invited talk. The papers deal with topics such as natural language processing; deep learning and embeddings; digital signal processing; modelling and reasoning; and machine learning applications.