Relevant bibliographies by topics / The Singing Detective

Contents

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

Academic literature on the topic 'The Singing Detective'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "The Singing Detective"

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Gontarski, S. E. "The Singing Detective Plays Beckett (Again)." Journal of Beckett Studies 15, no. 1-2 (January 2005): 242–47. http://dx.doi.org/10.3366/jobs.2006.15.1-2.21.

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Gras, Vernon. "Revisiting The Singing Detective decades later." Journal of Screenwriting 4, no. 3 (August 1, 2013): 305–7. http://dx.doi.org/10.1386/josc.4.3.305_7.

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Aubry, Danielle. "The Singing Detective: Dédales agonistiques d'une rédemption." University of Toronto Quarterly 73, no. 3 (July 2004): 847–61. http://dx.doi.org/10.3138/utq.73.3.847.

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Ganz, Adam. "Interview with Jon Amiel, Director of The Singing Detective." Journal of Screenwriting 4, no. 3 (August 1, 2013): 227–36. http://dx.doi.org/10.1386/josc.4.3.227_7.

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Cook, John R. "‘Message for Posterity’: The Singing Detective (1986) 25 years on." Journal of Screenwriting 4, no. 3 (August 1, 2013): 259–72. http://dx.doi.org/10.1386/josc.4.3.259_1.

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Kenneth Pellow, C. "The Function of “The Bloody Songs” in Dennis Potter's The Singing Detective." Journal of Popular Culture 46, no. 5 (October 2013): 1051–69. http://dx.doi.org/10.1111/jpcu.12066.

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Vickers, N. "Religious Irony and Freudian Rationalism in Dennis Potter's The Singing Detective (1986)." Literature and Theology 20, no. 4 (October 30, 2006): 411–23. http://dx.doi.org/10.1093/litthe/frl041.

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Corrigan, Timothy. "Back to the future in The Singing Detective: Amphibians, puzzles, and adaptations." Journal of Screenwriting 4, no. 3 (August 1, 2013): 237–45. http://dx.doi.org/10.1386/josc.4.3.237_7.

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Creeber, Glen. "And the beat goes on: The continuing influence of The Singing Detective." Journal of Screenwriting 4, no. 3 (August 1, 2013): 247–58. http://dx.doi.org/10.1386/josc.4.3.247_1.

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Qureshi, Faisal A. "The Singing Detective goes to Hollywood: An interview with director Keith Gordon." Journal of Screenwriting 4, no. 3 (August 1, 2013): 325–33. http://dx.doi.org/10.1386/josc.4.3.325_7.

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Dissertations / Theses on the topic "The Singing Detective"

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Evans, Gwynne Wheldon. "Out of the Limelight (a Cycle of Plays) and The Singing Detective and Out of the Limelight: a Comparative Study." Thesis, Bangor University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490423.

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This Ph.D. in Creative and Critical Writing is presented in two parts. The first part consists of a cycle of four original teleplays entitled Out of the Limelight, portraying the experiences of members of a theatre company. The main character in each case is drawn into a world of constructed realism. Each character reacts in an individualistic way, in accordance with their purpose. In some cases there is a remedy, brought about by the character himself or by an outside force. Sometimes a combination of both is required. In one case the result is tragic. The second part of the Ph.D., a critical response to the Creative Writing of the first part, is in the form of a comparative study between the four teleplays and The Singing Detective by Dennis Potter. The relationship between reality and constructed realism is apparent in Potter's text in that the main character, Philip Marlow, uses a constructed world both as an escape mechanism and as a means of self analysis. The techniques used by both playwrights (myself and Potter) will be analysed and comparisons made in order to show the use and purpose of a constructed world in the experience of the characters in both Out of the Limelight and The Singing Detective.
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Brie, Stephen Michael. "'Yesterday once more' : an investigation of the relationship between popular music, audience, and authorial intention in Dennis Potter's 'Pennies from heaven', 'The singing detective', and 'Lipstick on your collar'." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250379.

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Critical interpretations of Dennis Potter's television drama serials have tended to take a writer-centred perspective, focusing on establishing links between the dramatist's life and work. In analysing the popular music content of these texts, critics have consistently postulated the existence of Brechtian distanciation effects on an implied viewer. Although, in order to contextualise Potter's relationship with popular music, authorial intention is discussed, this study shifts the focus towards empirical interpretations of the musical sequences in Pennies from Heaven, The Singing Detective and Lipstick on Your Col/ar, and, in doing so, problematises the application of Brechtian theory to those texts. Utilising theoretical framings drawn from television studies, film studies, literary studies, communication studies, and musicology, the thesis offers interpretation and analysis of empirical material generated in response to both quantitative and qualitative exercises, and sets out to identify, and investigate, the narratological, musicological, and psychological factors which come into play when actual viewers encounter the narratively foregrounded, lip-synched musical sequences in Potter's serials. The influence of respondent age and gender, of implied author discourse, and of genre expectation on emprirical readings are also investigated. The thesis identifies, and attempts to account for, a predisposition on the part of Potter's musically-infused period dramas to stimulate susceptible viewers to drift away from the performance, and into nostalgic memory excursions, or fabricated imaginings, experiences which often result in narrative amnesia, an inability to subsequently recall and/or recollect elements of narrative detail.
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Nolan, Karin. "The Comparative Effectiveness of Teaching Beat Detection through Movement and Singing among Kindergarten Students." Thesis, The University of Arizona, 2007. http://hdl.handle.net/10150/193302.

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The purpose of this study was to determine if beat detection and beat competency (i.e. maintaining a steady beat) could be more effectively taught through movement, singing, or a combination of the two. Subjects (N =102) were kindergarten students from an elementary school in the Southwest. Students completed a pretest and posttest to assess beat detection ability; the test measured their ability to tap a steady beat with and without music. Subjects received instruction in one of three methods for a six-week period: singing, movement, or a combination of the two. Data analysis results revealed a significant (p < .05) difference between the pretest and the posttest scores for all three groups; subjects appeared to show progress in the ability to detect and maintain a beat with all three instructional methods. There was no significant difference, however, in the progress between the groups; each method of instruction yielded similar improvement.
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Milo, Sarah Khatcherian. "Guide of the Voice Teacher to Vocal Health for Voice Students: Preventing, Detecting, and Addressing Symptoms." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1399019362.

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Werder, Dominik. "Color Screening in QCD and Neutrinos from Singlino Dark Matter." Doctoral thesis, Uppsala universitet, Högenergifysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-267310.

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Hard diffraction in proton collisions, where the initial state proton emerges from the interaction rather undisturbed despite a hard interaction scale, has been studied for a few decades. First observed in proton-proton collisions, the phenomenon is seen as well in deep inelastic electron-proton scattering (DIS) as a leading final state proton and a rapidity gap-region without final state particles. Although a rather successful description in terms of the exchange of a hadronic color singlet pomeron with a parameterized gluon content exists, it is still an open question whether a theoretically more well-founded description can be obtained based on quantum chromodynamics. The soft color interaction model (SCI) attempts this through additional gluon exchanges at momentum scales below the conventional scale of perturbative QCD and the hadronization scale. Such gluons can lead to an effective color singlet exchange and therefore to diffraction. This thesis explores the phenomenology of the SCI model in diffractive W and photon+jet production. For diffractive deep inelastic scattering, a dynamic color screening model is developed based on a summed amplitude for soft gluon exchanges. The studies of the model within Monte Carlo event simulation show that the additional dynamics improve the description of electron-positron scattering data from HERA. Dijet events in proton-proton collisions with an upper limit on the energy flow between the jets is sensitive to large angle gluon emissions. This thesis applies a resummation method which takes into account also secondary emissions to describe this observable and shows that a good description of data from ATLAS can be achieved. Supersymmetric extensions to the Standard Model provide a possible explanation for dark matter in the universe. The next-to-minimal supersymmetric extension (NMSSM) can contain a dark matter candidate in form of the lightest neutralino with a substantial singlino component. This thesis studies the prospects for indirect detection of dark matter for such viable NMSSM model points via the observation of neutrinos from neutralino annihilation in the sun with IceCube and the future extension PINGU. It is shown that with a few years of data taking large parts of the parameter space can be excluded or a discovery be made.
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Gong, Rong. "Automatic assessment of singing voice pronunciation: a case study with Jingju music." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/664421.

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Online learning has altered music education remarkable in the last decade. Large and increasing amount of music performing learners participate in online music learning courses due to the easy-accessibility and boundless of time-space constraints. Singing can be considered the most basic form of music performing. Automatic singing voice assessment, as an important task in Music Information Retrieval (MIR), aims to extract musically meaningful information and measure the quality of learners' singing voice. Singing correctness and quality is culture-specific and its assessment requires culture-aware methodologies. Jingju (also known as Beijing opera) music is one of the representative music traditions in China and has spread to many places in the world where there are Chinese communities. Our goal is to tackle unexplored automatic singing voice pronunciation assessment problems in jingju music, to make the current eurogeneric assessment approaches more culture-aware, and in return, to develop new assessment approaches which can be generalized to other musical traditions.<br>El aprendizaje en línea ha cambiado notablemente la educación musical en la pasada década. Una cada vez mayor cantidad de estudiantes de interpretación musical participan en cursos de aprendizaje musical en línea por su fácil accesibilidad y no estar limitada por restricciones de tiempo y espacio. Puede considerarse el canto como la forma más básica de interpretación. La evaluación automática de la voz cantada, como tarea importante en la disciplina de Recuperación de Información Musical (MIR por sus siglas en inglés) tiene como objetivo la extracción de información musicalmente significativa y la medición de la calidad de la voz cantada del estudiante. La corrección y calidad del canto son específicas a cada cultura y su evaluación requiere metodologías con especificidad cultural. La música del jingju (también conocido como ópera de Beijing) es una de las tradiciones musicales más representativas de China y se ha difundido a muchos lugares del mundo donde existen comunidades chinas.Nuestro objetivo es abordar problemas aún no explorados sobre la evaluación automática de la voz cantada en la música del jingju, hacer que las propuestas eurogenéticas actuales sobre evaluación sean más específicas culturalmente, y al mismo tiempo, desarrollar nuevas propuestas sobre evaluación que puedan ser generalizables para otras tradiciones musicales.
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Allegro, Pedro Luís Cameira Sollari. "Singing voice detection in polyphonic music signals." Dissertação, 2008. http://hdl.handle.net/10216/57980.

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Estágio realizado no INESC e orientado pelo Prof. Fabien Gouyon<br>Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Ramo Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 2008
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Allegro, Pedro Luís Cameira Sollari. "Singing voice detection in polyphonic music signals." Master's thesis, 2008. http://hdl.handle.net/10216/57980.

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Estágio realizado no INESC e orientado pelo Prof. Fabien Gouyon<br>Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Ramo Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 2008
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Liu, Chih-Chun, and 劉至峻. "Deep Learning Algorithm Using Multi-model Combination Applied to Singing Voice Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4f4um8.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>106<br>Detecting the vocal sound in a piece of audio is a fundamental step to many advanced audio processing techniques. Previously, one study showed that good accuracy of 92% could be achievable for this problem by using the convolutional neural networks (CNN) using spectrogram as the input features. To explore the possibilities of further performance improvement, in this thesis we attempted to incorporate CNN and other neural network architectures, such as Long Short Term Memory (LSTM), Convolutional LSTM, and Capsule Networks, into ensemble learning. The ensemble learning approaches studied in this thesis includeed voting, fusion, and post classification, and the accuracy of each individual approach was reported. Regarding to the training/testing dataset, in addition to the well-known Jamendo dataset, we also built in-house datasets to validate the studied approaches. When using the Jamendo dataset, the average accuracy achieved 94.2% by using voting or post classification approach. This figure is higher than that of using any single architecture. When tested with the in-house datasets, voting or post classification approach also yielded better accuracy than a single model could achieve. Overall, this thesis confirmed that the ensemble learning was effective in terms of accuracy for the vocal detection problem.
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Huang, Hsin-Jung, and 黃信榮. "A Study on Note Detection and Melody Matching Method for Query By Singing/Humming System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/01357850967450510157.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>97<br>Onset detection for singing voices is an important but difficult problem for note detection in query by singing/humming or music transcription. The purpose of this paper is to improve the performance of onset detection for singing/humming voice. This paper proposes an onset detection scheme which utilizes the moving average filtering in detection function to accentuate the uprising margins, while making use of discriminative classifier based on Gaussian mixture models to combine relevant features of adjacent peaks in final decision. Experimental results show that the onset detection scheme can improve the detection performance significantly, and achieve 77.7% of precision rate and 76.9% of recall rate at 77.4% of F-measure. This onset detection scheme was further combined with the query by singing/humming system, and experimental results show that, the onset detection to detect note can effectively improve the performance of music search. The MRR value can be increased from 0.53 to 0.56 and increase the top-15 hit rate from 67% to 70% when onset detection is applied to the note detection.
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Books on the topic "The Singing Detective"

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Potter, Dennis. The singing detective. New York: Vintage Books, 1986.

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The singing detective. New York: Vintage Books, 1988.

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Potter, Dennis. The singing detective. New York: Vintage Books, 1988.

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The singing detective. London: Faber and Faber, 1986.

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Potter, Dennis. The singing detective: [screenplay]. London: Hollywood Scripts, 1990.

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The singing cave. Swords: Children's Poolbeg, 1991.

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Whitney, Phyllis A. The singing stones. London: Hodder & Stoughton, 1990.

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Whitney, Phyllis A. The singing stones. London: Coronet, 1991.

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Whitney, Phyllis A. The singing stones. London: Chivers, 1992.

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Whitney, Phyllis A. The singing stones. New York: Doubleday, 1990.

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Book chapters on the topic "The Singing Detective"

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Voigts-Virchow, Eckart. "Potter, Dennis: The Singing Detective." In Kindlers Literatur Lexikon (KLL), 1–2. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_14515-1.

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Miyagawa, Isao, Yuya Chiba, Takashi Nose, and Akinori Ito. "Detection of Singing Mistakes from Singing Voice." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 130–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63859-1_17.

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You, Shingchern D., and Yi-Chung Wu. "Comparative Study of Singing Voice Detection Methods." In Computer Science and its Applications, 1291–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45402-2_180.

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Rao, Vishweshwara, Chitralekha Gupta, and Preeti Rao. "Context-Aware Features for Singing Voice Detection in Polyphonic Music." In Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation, 43–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37425-8_4.

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Chen, Zhigao, Xulong Zhang, Jin Deng, Juanjuan Li, Yiliang Jiang, and Wei Li. "A Practical Singing Voice Detection System Based on GRU-RNN." In Lecture Notes in Electrical Engineering, 15–25. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8707-4_2.

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Stoller, Daniel, Sebastian Ewert, and Simon Dixon. "Jointly Detecting and Separating Singing Voice: A Multi-Task Approach." In Latent Variable Analysis and Signal Separation, 329–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_31.

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Zhang, Xulong, Shengchen Li, Zijin Li, Shizhe Chen, Yongwei Gao, and Wei Li. "Singing Voice Detection Using Multi-Feature Deep Fusion with CNN." In Lecture Notes in Electrical Engineering, 41–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-2756-2_4.

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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, 707–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_87.

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Mimilakis, Stylianos I., Christof Weiss, Vlora Arifi-Müller, Jakob Abeßer, and Meinard Müller. "Cross-version Singing Voice Detection in Opera Recordings: Challenges for Supervised Learning." In Machine Learning and Knowledge Discovery in Databases, 429–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_35.

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Neocleous, Andreas, George Azzopardi, Christos N. Schizas, and Nicolai Petkov. "Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music." In Computer Analysis of Images and Patterns, 558–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_47.

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Conference papers on the topic "The Singing Detective"

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Moura, Shayenne, and Marcelo Queiroz. "Instrumental Sensibility of Vocal Detector Based on Spectral Features." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10451.

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Detecting voice in a mixture of sound sources remains a challenging task in MIR research. The musical content can be perceived in many different ways as instrumentation varies. We evaluate how instrumentation affects singing voice detection in pieces using a standard spectral feature (MFCC). We trained Random Forest models with song remixes for specific subsets of sound sources, and compare it to models trained with the original songs. We thus present a preliminary analysis of the classification accuracy results.
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Shenoy, Arun, Yuansheng Wu, and Ye Wang. "Singing voice detection for karaoke application." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.631645.

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Nwe, Tin Lay, Arun Shenoy, and Ye Wang. "Singing voice detection in popular music." In the 12th annual ACM international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1027527.1027602.

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Paul, Soumava, Gurunath Reddy M, K. Sreenivasa Rao, and Partha Pratim Das. "Knowledge Distillation for Singing Voice Detection." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-636.

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Leonidas, Ioannidis, and Jean-Luc Rouas. "Exploiting Semantic Content for Singing Voice Detection." In 2012 IEEE Sixth International Conference on Semantic Computing (ICSC). IEEE, 2012. http://dx.doi.org/10.1109/icsc.2012.18.

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Tsai, Wei-Ho, Van-Thuan Tran, and Shiang-Shiun Kung. "Automatic Detection of Mispronounced Lyrics in Singing." In 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2019. http://dx.doi.org/10.1109/icmlc48188.2019.8949315.

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Nwe, Tin Lay, and Haizhou Li. "Singing voice detection using perceptually-motivated features." In the 15th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1291233.1291299.

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Pikrakis, Aggelos, Yannis Kopsinis, Nadine Kroher, and Jose-Miguel Diaz-Banez. "Unsupervised singing voice detection using dictionary learning." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760441.

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Lin, Tse-En, Chung-Chien Hsu, Yi-Cheng Chen, Jian-Hueng Chen, and Tai-Shih Chi. "Spectro-temporal modulation based singing detection combined with pitch-based grouping for singing voice separation." In Interspeech 2013. ISCA: ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-652.

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Leglaive, Simon, Romain Hennequin, and Roland Badeau. "Singing voice detection with deep recurrent neural networks." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7177944.

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