Proceedings of the 11th Conference on Sound and Music Technology: Revised Selected Papers from CSMT 2024

by Kun Qian (Editor), Li Zhou (Editor), Qinglin Meng (Editor), Yongwei Gao (Editor)

Overview

This book presents selected papers at the 11th Conference on Sound and Music Technology (CSMT) held in October 2024, Wuhan, China. CSMT is a multidisciplinary conference focusing on audio processing and understanding with bias on music and acoustic signals. The primary aim of the book is to promote the collaboration between art society and technical society in China. In this book, the paper included covers a wide range topic from speech, signal processing, music understanding, machine learning, and signal processing for advanced medical diagnosis and treatment applications, which demonstrates the target of CSMT merging arts and science research together. Its content caters to scholars, researchers, engineers, artists, and education practitioners not only from academia but also industry, who are interested in audio/acoustics analysis signal processing, music, sound, and artificial intelligence (AI).

Product Details

ISBN-13:	9789819647828
Publisher:	Springer Nature Singapore
Publication date:	07/23/2025
Series:	Lecture Notes in Electrical Engineering , #1404
Pages:	109
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

1. Meta-Learning for Domain Generalization in Anomalous Sound Detection.- 2. Online Joint Beat and Downbeat Tracking with Time Series Forecasting Model.- 3. Advancing Metadata-Convolutional Neural Networks with Multi-Supervised Contrastive Learning and Metadata Insights for Respiratory Sound Analysis.- 4. Automatic Performative Transcription of Guitar Music Based on Multimodal Network.- 5. A Framework for the Digital Representation and Rendering of Chinese Jianpu Notation for Constructing a Synthetic OMR Dataset.- 6. Accent Recognition with Auxiliary Task and Contrastive Learning.- 7. Effective Denoising in Music-Present Pubs with Efficient Channel Attention.- 7. Semi-Supervised Self-Learning Enhanced Music Emotion Recognition.