A study of the influence of audio signal processing technology on the expression of music aesthetics in piano performance

[1] Zölzer, U. (2022). Digital audio signal processing. John Wiley & Sons. ZölzerU. (2022). Digital audio signal processing. John Wiley & Sons.Search in Google Scholar

[2] Holzapfel, A., Sturm, B., & Coeckelbergh, M. (2018). Ethical dimensions of music information retrieval technology. Transactions of the International Society for Music Information Retrieval, 1(1), 44-55. HolzapfelA.SturmB. & CoeckelberghM. (2018). Ethical dimensions of music information retrieval technology. Transactions of the International Society for Music Information Retrieval, 1(1), 44-55.Search in Google Scholar

[3] Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S. Y., & Sainath, T. (2019). Deep learning for audio signal processing. IEEE Journal of Selected Topics in Signal Processing, 13(2), 206-219. PurwinsH.LiB.VirtanenT.SchlüterJ.ChangS. Y. & SainathT. (2019). Deep learning for audio signal processing. IEEE Journal of Selected Topics in Signal Processing, 13(2), 206-219.Search in Google Scholar

[4] Yuan, R., Ma, Y., Li, Y., Zhang, G., Chen, X., Yin, H., ... & Fu, J. (2023). Marble: Music audio representation benchmark for universal evaluation. Advances in Neural Information Processing Systems, 36, 39626-39647. YuanR.MaY.LiY.ZhangG.ChenX.YinH. ... & FuJ. (2023). Marble: Music audio representation benchmark for universal evaluation. Advances in Neural Information Processing Systems, 36, 39626-39647.Search in Google Scholar

[5] Nieto, O., Mysore, G. J., Wang, C. I., Smith, J. B., Schlüter, J., Grill, T., & McFee, B. (2020). Audio-based music structure analysis: Current trends, open challenges, and applications. Transactions of the International Society for Music Information Retrieval, 3(1). NietoO.MysoreG. J.WangC. I.SmithJ. B.SchlüterJ.GrillT. & McFeeB. (2020). Audio-based music structure analysis: Current trends, open challenges, and applications. Transactions of the International Society for Music Information Retrieval, 3(1).Search in Google Scholar

[6] Pons, J., Slizovskaia, O., Gong, R., Gómez, E., & Serra, X. (2017, August). Timbre analysis of music audio signals with convolutional neural networks. In 2017 25th European Signal Processing Conference (EUSIPCO) (pp. 2744-2748). IEEE. PonsJ.SlizovskaiaO.GongR.GómezE. & SerraX. (2017, August). Timbre analysis of music audio signals with convolutional neural networks. In 2017 25th European Signal Processing Conference (EUSIPCO) (pp. 2744-2748). IEEE.Search in Google Scholar

[7] Benetos, E., Dixon, S., Duan, Z., & Ewert, S. (2018). Automatic music transcription: An overview. IEEE Signal Processing Magazine, 36(1), 20-30. BenetosE.DixonS.DuanZ. & EwertS. (2018). Automatic music transcription: An overview. IEEE Signal Processing Magazine, 36(1), 20-30.Search in Google Scholar

[8] Solanki, A., & Pandey, S. (2022). Music instrument recognition using deep convolutional neural networks. International Journal of Information Technology, 14(3), 1659-1668. SolankiA. & PandeyS. (2022). Music instrument recognition using deep convolutional neural networks. International Journal of Information Technology, 14(3), 1659-1668.Search in Google Scholar

[9] Lerch, A. (2022). An introduction to audio content analysis: Music Information Retrieval tasks and applications. John Wiley & Sons. LerchA. (2022). An introduction to audio content analysis: Music Information Retrieval tasks and applications. John Wiley & Sons.Search in Google Scholar

[10] Agres, K. R., Schaefer, R. S., Volk, A., Van Hooren, S., Holzapfel, A., Dalla Bella, S., ... & Magee, W. L. (2021). Music, computing, and health: a roadmap for the current and future roles of music technology for health care and well-being. Music & Science, 4, 2059204321997709. AgresK. R.SchaeferR. S.VolkA.Van HoorenS.HolzapfelA.Dalla BellaS. ... & MageeW. L. (2021). Music, computing, and health: a roadmap for the current and future roles of music technology for health care and well-being. Music & Science, 4, 2059204321997709.Search in Google Scholar

[11] Cafaro, A., & Arneson, C. (2020). Audio technology: A tool for teachers and singers. Journal of Singing, 76(3), 311-16. CafaroA. & ArnesonC. (2020). Audio technology: A tool for teachers and singers. Journal of Singing, 76(3), 311-16.Search in Google Scholar

[12] Cano, E., FitzGerald, D., Liutkus, A., Plumbley, M. D., & Stöter, F. R. (2018). Musical source separation: An introduction. IEEE Signal Processing Magazine, 36(1), 31-40. CanoE.FitzGeraldD.LiutkusA.PlumbleyM. D. & StöterF. R. (2018). Musical source separation: An introduction. IEEE Signal Processing Magazine, 36(1), 31-40.Search in Google Scholar

[13] Simonetta, F., Ntalampiras, S., & Avanzini, F. (2019, January). Multimodal music information processing and retrieval: Survey and future challenges. In 2019 international workshop on multilayer music representation and processing (MMRP) (pp. 10-18). IEEE. SimonettaF.NtalampirasS. & AvanziniF. (2019, January). Multimodal music information processing and retrieval: Survey and future challenges. In 2019 international workshop on multilayer music representation and processing (MMRP) (pp. 10-18). IEEE.Search in Google Scholar

[14] Drott, E. A. (2018). Music as a Technology of Surveillance. Journal of the Society for American Music, 12(3), 233-267. DrottE. A. (2018). Music as a Technology of Surveillance. Journal of the Society for American Music, 12(3), 233-267.Search in Google Scholar

[15] Vincent, E., Virtanen, T., & Gannot, S. (Eds.). (2018). Audio source separation and speech enhancement. John Wiley & Sons. VincentE.VirtanenT. & GannotS. (Eds.). (2018). Audio source separation and speech enhancement. John Wiley & Sons.Search in Google Scholar

[16] Pons, J., & Serra, X. (2019, May). Randomly weighted cnns for (music) audio classification. In ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 336-340). IEEE. PonsJ. & SerraX. (2019, May). Randomly weighted cnns for (music) audio classification. In ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 336-340). IEEE.Search in Google Scholar

[17] Nam, J., Choi, K., Lee, J., Chou, S. Y., & Yang, Y. H. (2018). Deep learning for audio-based music classification and tagging: Teaching computers to distinguish rock from bach. IEEE signal processing magazine, 36(1), 41-51. NamJ.ChoiK.LeeJ.ChouS. Y. & YangY. H. (2018). Deep learning for audio-based music classification and tagging: Teaching computers to distinguish rock from bach. IEEE signal processing magazine, 36(1), 41-51.Search in Google Scholar

[18] Jensenius, A. R., & Lyons, M. J. (Eds.). (2017). A nime reader: Fifteen years of new interfaces for musical expression (Vol. 3). Springer. JenseniusA. R. & LyonsM. J. (Eds.). (2017). A nime reader: Fifteen years of new interfaces for musical expression (Vol. 3). Springer.Search in Google Scholar

[19] Presannakumar Krishna & Mohamed Anuj. (2023). Source identification of weak audio signals using attention based convolutional neural network. Applied Intelligence(22),27044-27059. KrishnaPresannakumar & AnujMohamed. (2023). Source identification of weak audio signals using attention based convolutional neural network. Applied Intelligence(22),27044-27059.Search in Google Scholar

[20] Mateusz Materlak & Ewelina Majda Zdancewicz. (2023). Classification of Engine Type of Vehicle Based on Audio Signal as a Source of Identification. Electronics(9), MaterlakMateusz & ZdancewiczEwelina Majda. (2023). Classification of Engine Type of Vehicle Based on Audio Signal as a Source of Identification. Electronics(9),Search in Google Scholar

[21] Zehua Ying,Zixuan Yan,Xuting Guo,Cunhao Li,Guoxiang Li,Xingli He & Wenlong Li. (2025). Portable Raman spectroscopy and fourier transform near infrared spectroscopy for the quantification of different sinomenine hydrochloride crystal forms. Journal of Pharmaceutical and Biomedical Analysis116567-116567. YingZehuaYanZixuanGuoXutingLiCunhaoLiGuoxiangHeXingli & LiWenlong. (2025). Portable Raman spectroscopy and fourier transform near infrared spectroscopy for the quantification of different sinomenine hydrochloride crystal forms. Journal of Pharmaceutical and Biomedical Analysis116567-116567.Search in Google Scholar

[22] Ehsan Rahimi & Pinliang Dong. (2024). Assessing Landscape Fragmentation Dynamics with Fourier Transforms. Journal of Landscape Ecology(3),97-112. RahimiEhsan & DongPinliang. (2024). Assessing Landscape Fragmentation Dynamics with Fourier Transforms. Journal of Landscape Ecology(3),97-112.Search in Google Scholar

[23] Xinyi Liu,Di Wang,Rui Wang,Bin Hu,Jinbang Wang,Yali Liu... & Weihua Feng. (2025). Integrating progressive screening strategy-based continuous wavelet transform with EfficientNetV2 for enhanced near-infrared spectroscopy.Talanta127188-127188. LiuXinyiWangDiWangRuiHuBinWangJinbangLiuYali... & FengWeihua. (2025). Integrating progressive screening strategy-based continuous wavelet transform with EfficientNetV2 for enhanced near-infrared spectroscopy.Talanta127188-127188.Search in Google Scholar

[24] Arasti Afrasiabi,Asaad Faramarzi,David Chapman & Alireza Keshavarzi. (2025). Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities. Journal of Applied Geophysics105567-105567. AfrasiabiArastiFaramarziAsaadChapmanDavid & KeshavarziAlireza. (2025). Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities. Journal of Applied Geophysics105567-105567.Search in Google Scholar