Empowering Aspiring Artists: A Machine Learning-Powered Carnatic Music Tutor
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3817-5_17
Reference10 articles.
1. Bhargave O (2019) Intelligent error detection and advisory system for practitioners of music using audio signal processing and computing. In: 2019 1st International conference on advanced technologies in intelligent control, environment, computing & communication engineering (ICATIECE). IEEE, pp 257–260
2. John S, Sinith M, Sudheesh R, Lalu P (2020) Classification of Indian classical carnatic music based on raga using deep learning. In: 2020 IEEE Recent advances in intelligent computational systems (RAICS). IEEE, pp 110–113
3. Gowrishankar B, Bhajantri NU (2022) Deep learning long short-term memory based automatic music transcription system for Carnatic music. In: 2022 IEEE International conference on distributed computing and electrical circuits and electronics (ICDCECE). IEEE, pp 1–6
4. Gfeller B, Frank C, Roblek D, Sharifi M, Tagliasacchi M, Velimirović M (2020) Spice: Self-supervised pitch estimation. IEEE/ACM Trans Audio Speech Lang Process 28:1118–1128
5. Radford A, Kim JW, Xu T, Brockman G, McLeavey C, Sutskever I (2023) Robust speech recognition via large-scale weak supervision. In: International conference on machine learning. PMLR, pp 28492–28518
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