Musical Signal Processing - A Literature Survey -- Noise Removal from Audio Using CNN and Denoiser -- Sine-Wave Speech as Pre-processing for Downstream Tasks -- Style of Vocal Singers in Indian Classical Music: Timbre Approach -- Style Identification of Vocal Singers in Indian Classical Music Using Meend and Andolan -- Vocalist Identification in Audio Songs Using Convolutional Neural Network -- Swaragram: Shruti-based Chromagram for Indian Classical Music -- An Artificial Intelligence-based Approach Towards Segregation of Folk Songs -- Shruti Detection Using Machine Learning and Sargam Identification for Instrumental Audio -- Addressing the Recitative Problem in Real-time Opera Tracking -- Perception of Similarity and Dissimilarity in Hindustani Classical Music -- Analytical Comparison of Classification Models for Raga Identification in Carnatic Classical Audio -- Multimodal Sentiment Analysis of Rabindra- Sangeet through Machine Learning Techniques.

This book features original papers from 25th International Symposium on Frontiers of Research in Speech and Music (FRSM 2020), jointly organized by National Institute of Technology, Silchar, India, during 8-9 October 2020. The book is organized in five sections, considering both technological advancement and interdisciplinary nature of speech and music processing. The first section contains chapters covering the foundations of both vocal and instrumental music processing. The second section includes chapters related to computational techniques involved in the speech and music domain. A lot of research is being performed within the music information retrieval domain which is potentially interesting for most users of computers and the Internet. Therefore, the third section is dedicated to the chapters related to music information retrieval. The fourth section contains chapters on the brain signal analysis and human cognition or perception of speech and music. The final section consists of chapters on spoken language processing and applications of speech processing.