Literatura académica sobre el tema "The Singing Detective"
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Artículos de revistas sobre el tema "The Singing Detective"
Gontarski, S. E. "The Singing Detective Plays Beckett (Again)". Journal of Beckett Studies 15, n.º 1-2 (enero de 2005): 242–47. http://dx.doi.org/10.3366/jobs.2006.15.1-2.21.
Texto completoGras, Vernon. "Revisiting The Singing Detective decades later". Journal of Screenwriting 4, n.º 3 (1 de agosto de 2013): 305–7. http://dx.doi.org/10.1386/josc.4.3.305_7.
Texto completoAubry, Danielle. "The Singing Detective: Dédales agonistiques d'une rédemption". University of Toronto Quarterly 73, n.º 3 (julio de 2004): 847–61. http://dx.doi.org/10.3138/utq.73.3.847.
Texto completoGanz, Adam. "Interview with Jon Amiel, Director of The Singing Detective". Journal of Screenwriting 4, n.º 3 (1 de agosto de 2013): 227–36. http://dx.doi.org/10.1386/josc.4.3.227_7.
Texto completoCook, John R. "‘Message for Posterity’: The Singing Detective (1986) 25 years on". Journal of Screenwriting 4, n.º 3 (1 de agosto de 2013): 259–72. http://dx.doi.org/10.1386/josc.4.3.259_1.
Texto completoKenneth Pellow, C. "The Function of “The Bloody Songs” in Dennis Potter's The Singing Detective". Journal of Popular Culture 46, n.º 5 (octubre de 2013): 1051–69. http://dx.doi.org/10.1111/jpcu.12066.
Texto completoVickers, N. "Religious Irony and Freudian Rationalism in Dennis Potter's The Singing Detective (1986)". Literature and Theology 20, n.º 4 (30 de octubre de 2006): 411–23. http://dx.doi.org/10.1093/litthe/frl041.
Texto completoCorrigan, Timothy. "Back to the future in The Singing Detective: Amphibians, puzzles, and adaptations". Journal of Screenwriting 4, n.º 3 (1 de agosto de 2013): 237–45. http://dx.doi.org/10.1386/josc.4.3.237_7.
Texto completoCreeber, Glen. "And the beat goes on: The continuing influence of The Singing Detective". Journal of Screenwriting 4, n.º 3 (1 de agosto de 2013): 247–58. http://dx.doi.org/10.1386/josc.4.3.247_1.
Texto completoQureshi, Faisal A. "The Singing Detective goes to Hollywood: An interview with director Keith Gordon". Journal of Screenwriting 4, n.º 3 (1 de agosto de 2013): 325–33. http://dx.doi.org/10.1386/josc.4.3.325_7.
Texto completoTesis sobre el tema "The Singing Detective"
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.
Texto completoBrie, 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.
Texto completoNolan, 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.
Texto completoMilo, 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.
Texto completoWerder, 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.
Texto completoGong, 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.
Texto completoEl 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.
Allegro, Pedro Luís Cameira Sollari. "Singing voice detection in polyphonic music signals". Dissertação, 2008. http://hdl.handle.net/10216/57980.
Texto completoTese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Ramo Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 2008
Allegro, Pedro Luís Cameira Sollari. "Singing voice detection in polyphonic music signals". Master's thesis, 2008. http://hdl.handle.net/10216/57980.
Texto completoTese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Ramo Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 2008
Liu, Chih-Chun y 劉至峻. "Deep Learning Algorithm Using Multi-model Combination Applied to Singing Voice Detection". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4f4um8.
Texto completo國立臺北科技大學
資訊工程系
106
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.
Huang, Hsin-Jung y 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.
Texto completo國立臺灣科技大學
資訊管理系
97
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.
Libros sobre el tema "The Singing Detective"
Potter, Dennis. The singing detective. New York: Vintage Books, 1986.
Buscar texto completoThe singing detective. New York: Vintage Books, 1988.
Buscar texto completoPotter, Dennis. The singing detective. New York: Vintage Books, 1988.
Buscar texto completoPotter, Dennis. The singing detective. London: Faber and Faber, 1986.
Buscar texto completoPotter, Dennis. The singing detective: [screenplay]. London: Hollywood Scripts, 1990.
Buscar texto completoThe singing cave. Swords: Children's Poolbeg, 1991.
Buscar texto completoWhitney, Phyllis A. The singing stones. London: Hodder & Stoughton, 1990.
Buscar texto completoWhitney, Phyllis A. The singing stones. London: Coronet, 1991.
Buscar texto completoWhitney, Phyllis A. The singing stones. London: Chivers, 1992.
Buscar texto completoWhitney, Phyllis A. The singing stones. New York: Doubleday, 1990.
Buscar texto completoCapítulos de libros sobre el tema "The Singing Detective"
Voigts-Virchow, Eckart. "Potter, Dennis: The Singing Detective". En Kindlers Literatur Lexikon (KLL), 1–2. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_14515-1.
Texto completoMiyagawa, Isao, Yuya Chiba, Takashi Nose y Akinori Ito. "Detection of Singing Mistakes from Singing Voice". En 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.
Texto completoYou, Shingchern D. y Yi-Chung Wu. "Comparative Study of Singing Voice Detection Methods". En 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.
Texto completoRao, Vishweshwara, Chitralekha Gupta y Preeti Rao. "Context-Aware Features for Singing Voice Detection in Polyphonic Music". En 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.
Texto completoChen, Zhigao, Xulong Zhang, Jin Deng, Juanjuan Li, Yiliang Jiang y Wei Li. "A Practical Singing Voice Detection System Based on GRU-RNN". En Lecture Notes in Electrical Engineering, 15–25. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8707-4_2.
Texto completoStoller, Daniel, Sebastian Ewert y Simon Dixon. "Jointly Detecting and Separating Singing Voice: A Multi-Task Approach". En 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.
Texto completoZhang, Xulong, Shengchen Li, Zijin Li, Shizhe Chen, Yongwei Gao y Wei Li. "Singing Voice Detection Using Multi-Feature Deep Fusion with CNN". En Lecture Notes in Electrical Engineering, 41–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-2756-2_4.
Texto completoRocamora, Martín y Alvaro Pardo. "Separation and Classification of Harmonic Sounds for Singing Voice Detection". En 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.
Texto completoMimilakis, Stylianos I., Christof Weiss, Vlora Arifi-Müller, Jakob Abeßer y Meinard Müller. "Cross-version Singing Voice Detection in Opera Recordings: Challenges for Supervised Learning". En 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.
Texto completoNeocleous, Andreas, George Azzopardi, Christos N. Schizas y Nicolai Petkov. "Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music". En 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.
Texto completoActas de conferencias sobre el tema "The Singing Detective"
Moura, Shayenne y Marcelo Queiroz. "Instrumental Sensibility of Vocal Detector Based on Spectral Features". En Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10451.
Texto completoShenoy, Arun, Yuansheng Wu y Ye Wang. "Singing voice detection for karaoke application". En Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.631645.
Texto completoNwe, Tin Lay, Arun Shenoy y Ye Wang. "Singing voice detection in popular music". En the 12th annual ACM international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1027527.1027602.
Texto completoPaul, Soumava, Gurunath Reddy M, K. Sreenivasa Rao y Partha Pratim Das. "Knowledge Distillation for Singing Voice Detection". En Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-636.
Texto completoLeonidas, Ioannidis y Jean-Luc Rouas. "Exploiting Semantic Content for Singing Voice Detection". En 2012 IEEE Sixth International Conference on Semantic Computing (ICSC). IEEE, 2012. http://dx.doi.org/10.1109/icsc.2012.18.
Texto completoTsai, Wei-Ho, Van-Thuan Tran y Shiang-Shiun Kung. "Automatic Detection of Mispronounced Lyrics in Singing". En 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2019. http://dx.doi.org/10.1109/icmlc48188.2019.8949315.
Texto completoNwe, Tin Lay y Haizhou Li. "Singing voice detection using perceptually-motivated features". En the 15th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1291233.1291299.
Texto completoPikrakis, Aggelos, Yannis Kopsinis, Nadine Kroher y Jose-Miguel Diaz-Banez. "Unsupervised singing voice detection using dictionary learning". En 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760441.
Texto completoLin, Tse-En, Chung-Chien Hsu, Yi-Cheng Chen, Jian-Hueng Chen y Tai-Shih Chi. "Spectro-temporal modulation based singing detection combined with pitch-based grouping for singing voice separation". En Interspeech 2013. ISCA: ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-652.
Texto completoLeglaive, Simon, Romain Hennequin y Roland Badeau. "Singing voice detection with deep recurrent neural networks". En 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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