Relevant bibliographies by topics / Singing voice recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Singing voice recognition'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Singing voice recognition"

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Wu, Wenqin, and Joonwhoan Lee. "Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations." Applied Sciences 14, no. 18 (2024): 8532. http://dx.doi.org/10.3390/app14188532.

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In general, it is difficult to obtain a huge, labeled dataset for deep learning-based phoneme recognition in singing voices. Studying singing voices also offers inherent challenges, compared to speech, because of the distinct variations in pitch, duration, and intensity. This paper proposes a detouring method to overcome this insufficient dataset, and applies it to the recognition of Korean phonemes in singing voices. The method started with pre-training the HuBERT, a self-supervised speech representation model, on a large-scale English corpus. The model was then adapted to the Korean speech d
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Wang, Xiaochen, and Tao Wang. "Voice Recognition and Evaluation of Vocal Music Based on Neural Network." Computational Intelligence and Neuroscience 2022 (May 20, 2022): 1–9. http://dx.doi.org/10.1155/2022/3466987.

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Artistic voice is the artistic life of professional voice users. In the process of selecting and cultivating artistic performing talents, the evaluation of voice even occupies a very important position. Therefore, an appropriate evaluation of the artistic voice is crucial. With the development of art education, how to scientifically evaluate artistic voice training methods and fairly select artistic voice talents is an urgent need for objective evaluation of artistic voice. The current evaluation methods for artistic voices are time-consuming, laborious, and highly subjective. In the objective
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Liusong, Yang, and Du Hui. "Voice Quality Evaluation of Singing Art Based on 1DCNN Model." Mathematical Problems in Engineering 2022 (July 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/2074844.

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Traditional speech recognition still has the problems of poor robustness and low signal-to-noise ratio, which makes the accuracy of speech recognition not ideal. Combining the idea of one-dimensional convolutional neural network with objective evaluation, an improved CNN speech recognition method is proposed in this paper. The simulation experiment is carried out with MATLAB. The effectiveness and feasibility of this method are verified by simulation. This new method is based on one-dimensional convolutional neural network. The traditional 1DNN algorithm is optimized by using the fractional pr
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Huang, Chunyuan. "Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/4572904.

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In the process of vocal music learning, incorrect vocalization methods and excessive use of voice have brought many problems to the voice and accumulated a lot of inflammation, so that the level of vocal music learning stagnated or even declined. How to find a way to improve yourself without damaging your voice has become a problem that we have been pursuing. Therefore, it is of great practical significance for vocal music teaching in normal universities to conduct in-depth research and discussion on “pharyngeal singing.” Based on audio extraction, this paper studies the vocal music teaching p
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Owen, Ceri. "On Singing and Listening in Vaughan Williams's Early Songs." 19th-Century Music 40, no. 3 (2017): 257–82. http://dx.doi.org/10.1525/ncm.2017.40.3.257.

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Vaughan Williams's celebrated set of Robert Louis Stevenson settings, Songs of Travel, has lately garnered liberal scholarly attention, not least on account of the vicissitudes of its publication history. Following the cycle's premiere in 1904 it was issued in two separate books, each gathering stylistically different songs. Though a credible case for narrative coherence has been advanced in numerous accounts, the cycle's peculiar amalgamation of materials might rather be read as a signal to its projection of multiple voices, which unsettle the longstanding critical tendency to map a single pr
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Muhathir, R. Muliono, N. Khairina, M. K. Harahap, and S. M. Putri. "Analysis Discrete Hartley Transform for the recognition of female voice based on voice register in singing techniques." Journal of Physics: Conference Series 1361 (November 2019): 012039. http://dx.doi.org/10.1088/1742-6596/1361/1/012039.

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Yuan, Weitao, Boxin He, Shengbei Wang, Jianming Wang, and Masashi Unoki. "Enhanced feature network for monaural singing voice separation." Speech Communication 106 (January 2019): 1–6. http://dx.doi.org/10.1016/j.specom.2018.11.004.

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Hu, Meihui, Zhiwei Xiang, and Kai Li. "Application of Artificial Intelligence Voice Technology in Radio and Television Media." Journal of Physics: Conference Series 2031, no. 1 (2021): 012051. http://dx.doi.org/10.1088/1742-6596/2031/1/012051.

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Abstract With the application of artificial intelligence in various fields, more and more people see the contribution of artificial intelligence technology to the development of the industry, and put more energy in the research of artificial intelligence language, and strive to provide better technical support for the development of more industries. In radio and television media, artificial intelligence voice technology can play a very important value, it can effectively improve the efficiency and quality of traditional audio work, optimize the singing system, broadcasting system and retrieval
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Liu, Pengfei, Wenjin Deng, Hengda Li, et al. "MusicFace: Music-driven expressive singing face synthesis." Computational Visual Media 10, no. 1 (2023): 119–36. http://dx.doi.org/10.1007/s41095-023-0343-7.

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AbstractIt remains an interesting and challenging problem to synthesize a vivid and realistic singing face driven by music. In this paper, we present a method for this task with natural motions for the lips, facial expression, head pose, and eyes. Due to the coupling of mixed information for the human voice and backing music in common music audio signals, we design a decouple-and-fuse strategy to tackle the challenge. We first decompose the input music audio into a human voice stream and a backing music stream. Due to the implicit and complicated correlation between the two-stream input signal
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Liu, Lilin. "The New Approach Research on Singing Voice Detection Algorithm Based on Enhanced Reconstruction Residual Network." Journal of Mathematics 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/7987592.

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With the development of Internet technology, multimedia information resources are increasing rapidly. Faced with the massive resources in the multimedia music library, it is extremely difficult for people to find the target music that meets their needs. How to realize computer analysis and perceive users’ needs for music resources has become the goal of the future development of human-computer interaction capabilities. Content-based music information retrieval applications are mainly embodied in the automatic classification and recognition of music. Traditional feedforward neural networks are
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Dissertations / Theses on the topic "Singing voice recognition"

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Regnier, Lise. "Localization, Characterization and Recognition of Singing Voices." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00687475.

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This dissertation is concerned with the problem of describing the singing voice within the audio signal of a song. This work is motivated by the fact that the lead vocal is the element that attracts the attention of most listeners. For this reason it is common for music listeners to organize and browse music collections using information related to the singing voice such as the singer name. Our research concentrates on the three major problems of music information retrieval: the localization of the source to be described (i.e. the recognition of the elements corresponding to the singing voice
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Vaglio, Andrea. "Leveraging lyrics from audio for MIR." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT027.

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Les paroles de chansons fournissent un grand nombre d’informations sur la musique car ellescontiennent une grande partie de la sémantique des chansons. Ces informations pourraient aider les utilisateurs à naviguer facilement dans une large collection de chansons et permettre de leur offrir des recommandations personnalisées. Cependant, ces informations ne sont souvent pas disponibles sous leur forme textuelle. Les systèmes de reconnaissance de la voix chantée pourraient être utilisés pour obtenir des transcriptions directement à partir de la source audio. Ces approches sont usuellement adaptée
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Marxer, Piñón Ricard. "Audio source separation for music in low-latency and high-latency scenarios." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123808.

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Aquesta tesi proposa mètodes per tractar les limitacions de les tècniques existents de separació de fonts musicals en condicions de baixa i alta latència. En primer lloc, ens centrem en els mètodes amb un baix cost computacional i baixa latència. Proposem l'ús de la regularització de Tikhonov com a mètode de descomposició de l'espectre en el context de baixa latència. El comparem amb les tècniques existents en tasques d'estimació i seguiment dels tons, que són passos crucials en molts mètodes de separació. A continuació utilitzem i avaluem el mètode de descomposició de l'espectre en tasques de
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Chung, Nien-Yu, and 鍾念佑. "Recognition of Singing Voice and Instrument Sound Using Combinations of Acoustic Features." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/39449100792026503384.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>104<br>This thesis aims to recognize the class that an input sound clip belongs to. The two sound classes concerned here are singing sound (with vocal singing) and instrument sound (without vocal singing). The focus of this research is placed on testing different combinations of those considered acoustic features in order to find a most effective feature vector for sound class recognition. The acoustic coefficients considered here include mel-frequency cepstral coefficients (MFCC), pitch-detection coefficients (PDC), Chroma extended features, and their delta coeffici
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Pereira, Ana Isabel Lemos do Carmo. "The influence of singing with text and a neutral syllable on Portuguese children´s vocal performance, song recognition, and use of singing voice." Doctoral thesis, 2019. http://hdl.handle.net/10362/91276.

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Research on children’s singing development is extensive. Different ages, approaches, and variables have been considered. However, research on singing with a neutral syllable versus singing with text is scarce and findings are inconclusive. Furthermore, little is known about children’s song recognition, and how text and melody interact along the learning process. In addition, the ability to use all vocal registers has not been of regular concern when investigating singing accuracy. Yet, it has been considered a pre-requisite towards accurate singing. The purpose of this dissertation was
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Book chapters on the topic "Singing voice recognition"

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Żwan, Paweł, Piotr Szczuko, Bożena Kostek, and Andrzej Czyżewski. "Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets." In Transactions on Rough Sets IX. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89876-4_25.

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Rocamora, Martín, and Alvaro Pardo. "Separation and Classification of Harmonic Sounds for Singing Voice Detection." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_87.

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Jefferson, Ann. "The Romantic Poet and the Brotherhood of Genius." In Genius in France. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691160658.003.0006.

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This chapter traces the emergence of a new poetry that presents its credentials as lying not with any preexisting literary or national tradition, but with the genius of the individual poet. Despite the real success of the volumes published in this spirit, the poet is portrayed, like Moses abandoned by his people, as having no public. The collective “other” that might afford him recognition is absent, and in the words of Victor Hugo, his was a voice crying in the wilderness, and singing to the deaf. The new poets thus enter the literary field announcing in advance that they will go unheard by a
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Conference papers on the topic "Singing voice recognition"

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Zhou, Huali, Yueqian Lin, Yao Shi, Peng Sun, and Ming Li. "Bisinger: Bilingual Singing Voice Synthesis." In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2023. http://dx.doi.org/10.1109/asru57964.2023.10389659.

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Gao, Xiaoxue, Xiaohai Tian, Yi Zhou, Rohan Kumar Das, and Haizhou Li. "Personalized Singing Voice Generation Using WaveRNN." In Odyssey 2020 The Speaker and Language Recognition Workshop. ISCA, 2020. http://dx.doi.org/10.21437/odyssey.2020-36.

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Huang, Wen-Chin, Lester Phillip Violeta, Songxiang Liu, Jiatong Shi, and Tomoki Toda. "The Singing Voice Conversion Challenge 2023." In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2023. http://dx.doi.org/10.1109/asru57964.2023.10389671.

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Wang, Jun-You, Hung-Yi Lee, Jyh-Shing Roger Jang, and Li Su. "Zero-Shot Singing Voice Synthesis from Musical Score." In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2023. http://dx.doi.org/10.1109/asru57964.2023.10389711.

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Liu, Ruolan, Xue Wen, Chunhui Lu, Liming Song, and June Sig Sung. "Vibrato Learning in Multi-Singer Singing Voice Synthesis." In 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2021. http://dx.doi.org/10.1109/asru51503.2021.9688029.

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Suzuki, Motoyuki, Sho Tomita, and Tomoki Morita. "Lyrics Recognition from Singing Voice Focused on Correspondence Between Voice and Notes." In Interspeech 2019. ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1318.

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Khunarsal, Peerapol, Chidchanok Lursinsap, and Thanapant Raicharoen. "Singing voice recognition based on matching of spectrogram pattern." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5179014.

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Liu, Songxiang, Yuewen Cao, Dan Su, and Helen Meng. "DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion." In 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2021. http://dx.doi.org/10.1109/asru51503.2021.9688219.

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Chowdhury, Anurag, Austin Cozzo, and Arun Ross. "Domain Adaptation for Speaker Recognition in Singing and Spoken Voice." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746111.

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Yamamoto, Ryuichi, Reo Yoneyama, Lester Phillip Violeta, Wen-Chin Huang, and Tomoki Toda. "A Comparative Study of Voice Conversion Models With Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023." In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2023. http://dx.doi.org/10.1109/asru57964.2023.10389779.

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