Pitch-Dependent Identification of Musical Instrument Sounds

Author:

Kitahara Tetsuro,Goto Masataka,Okuno Hiroshi G.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference8 articles.

1. J.C. Brown, “Computer identification of musical instruments using pattern recognition with cepstral coefficients as features,” Journal of Acoustic Society of America vol. 103, no. 3, pp. 1933–1941, 1999.

2. A. Eronen and A. Klapuri, “Musical instrument recognition using cepstral coefficients and temporal features,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing, IEEE, 2000, pp. 753–756.

3. I. Fujinaga and K. MacMillan, “Realtime recognition of orchestral instruments,” in Proceedings of International Computer Music Conference, 2000, pp. 141–143.

4. K. Kashino, K. Nakadai, T. Kinoshita, and H. Tanaka, “Application of the bayesian probability network to music scene analysis,” in Computational Auditory Scene Analysis, edited by D. Rosenthal and H.~G. Okuno, Eds., Lawrence Erlbaum Associates, 1998, pp. 115–137.

5. K.D. Martin, “Sound-Source Recognition: A Theory and Computational Model,” Ph.D. Thesis, MIT, 1999.

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Learning-Based Automatic Music Transcription of the Diwdiw-as, a Native Filipino Bamboo Flute;2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM);2023-11-19

2. Designing a Training Set for Musical Instruments Identification;Computational Science – ICCS 2022;2022

3. Infant Pitch and Timbre Discrimination in the Presence of Variation in the Other Dimension;Journal of the Association for Research in Otolaryngology;2021-09-14

4. Optimization of Feature Selection and Classification of Oriental Music Instruments Identification;2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS);2019-09

5. MISNA - A musical instrument segregation system from noisy audio with LPCC-S features and extreme learning;Multimedia Tools and Applications;2018-04-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3