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

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  • Formatas: Hardback, 109 pages, aukštis x plotis: 235x155 mm, 28 Illustrations, color; 15 Illustrations, black and white; VII, 109 p. 43 illus., 28 illus. in color., 1 Hardback
  • Serija: Lecture Notes in Electrical Engineering 1404
  • Išleidimo metai: 22-Aug-2025
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 9819647827
  • ISBN-13: 9789819647828
Kitos knygos pagal šią temą:
Proceedings of the 11th Conference on Sound and Music Technology: Revised Selected Papers from CSMT 2024Didesnis paveikslėlis
  • Formatas: Hardback, 109 pages, aukštis x plotis: 235x155 mm, 28 Illustrations, color; 15 Illustrations, black and white; VII, 109 p. 43 illus., 28 illus. in color., 1 Hardback
  • Serija: Lecture Notes in Electrical Engineering 1404
  • Išleidimo metai: 22-Aug-2025
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 9819647827
  • ISBN-13: 9789819647828
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9789819647828.html

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).

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.