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Journal articles on the topic 'The Singing Detective'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Karpf, Anne. "The pleasure of immersion: Some thoughts on how The Singing Detective sustains narrative." Journal of Screenwriting 4, no. 3 (August 1, 2013): 309–15. http://dx.doi.org/10.1386/josc.4.3.309_1.

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12

Rolinson, David. "Report on The Singing Detective 25th Anniversary Symposium, University of London, 10 December 2011." Journal of Screenwriting 4, no. 3 (August 1, 2013): 335–43. http://dx.doi.org/10.1386/josc.4.3.335_7.

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13

Vanheste. "Interpreting Clues: Human Life and Narrative Identity in Dennis Potter’s The Singing Detective." Soundings: An Interdisciplinary Journal 103, no. 3 (2020): 367. http://dx.doi.org/10.5325/soundings.103.3.0367.

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14

Haswell, Janis E. "James Lee Burke’s Theology of Redemption." Studies in Linguistics and Literature 4, no. 3 (August 27, 2020): p119. http://dx.doi.org/10.22158/sll.v4n3p119.

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In his 23 novels featuring character Dave Robicheaux, James Lee Burke poses a fundamentally moral (and unanswerable) question: how does a good man maintain himself in the face of evil? From Neon Rain (1987) to A Private Cathedral (2020), Burke develops Robicheaux as both a detective and a narrator, reminiscent of the pastoral genre, where shepherds spent their leisurely, idyllic summer days in singing contests. Like the classical shepherds/poets double, Robicheaux is cop/storyteller and seeks to redeem criminals in both capacities. Burke ultimately illustrates how good and evil will always, mu

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15

Reek, J. "'But Now My Eye Sees You': Reading Dennis Potter's The Singing Detective and the Book of Job with Helene Cixous." Literature and Theology 29, no. 2 (July 10, 2014): 199–215. http://dx.doi.org/10.1093/litthe/fru047.

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16

Kum, Sangeun, and Juhan Nam. "Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks." Applied Sciences 9, no. 7 (March 29, 2019): 1324. http://dx.doi.org/10.3390/app9071324.

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Singing melody extraction essentially involves two tasks: one is detecting the activity of a singing voice in polyphonic music, and the other is estimating the pitch of a singing voice in the detected voiced segments. In this paper, we present a joint detection and classification (JDC) network that conducts the singing voice detection and the pitch estimation simultaneously. The JDC network is composed of the main network that predicts the pitch contours of the singing melody and an auxiliary network that facilitates the detection of the singing voice. The main network is built with a convolut

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17

Zhang, Xulong, Yi Yu, Yongwei Gao, Xi Chen, and Wei Li. "Research on Singing Voice Detection Based on a Long-Term Recurrent Convolutional Network with Vocal Separation and Temporal Smoothing." Electronics 9, no. 9 (September 7, 2020): 1458. http://dx.doi.org/10.3390/electronics9091458.

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Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extrac

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18

Sarasola, Xabier, Eva Navas, David Tavarez, Luis Serrano, Ibon Saratxaga, and Inma Hernaez. "Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification." Applied Sciences 9, no. 15 (August 2, 2019): 3140. http://dx.doi.org/10.3390/app9153140.

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Speech and singing voice discrimination is an important task in the speech processing area given that each type of voice requires different information retrieval and signal processing techniques. This discrimination task is hard even for humans depending on the length of voice segments. In this article, we present an automatic speech and singing voice classification method using pitch parameters derived from musical note information and f 0 stability analysis. We applied our method to a database containing speech and a capella singing and compared the results with other discrimination techniqu

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19

Krause, Michael, Meinard Müller, and Christof Weiß. "Singing Voice Detection in Opera Recordings: A Case Study on Robustness and Generalization." Electronics 10, no. 10 (May 20, 2021): 1214. http://dx.doi.org/10.3390/electronics10101214.

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Automatically detecting the presence of singing in music audio recordings is a central task within music information retrieval. While modern machine-learning systems produce high-quality results on this task, the reported experiments are usually limited to popular music and the trained systems often overfit to confounding factors. In this paper, we aim to gain a deeper understanding of such machine-learning methods and investigate their robustness in a challenging opera scenario. To this end, we compare two state-of-the-art methods for singing voice detection based on supervised learning: A tr

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20

Liu, Yu Xiang, Ze Yu Jin, Jia Jia, and Lian Hong Cai. "An Automatic Singing Evaluation System." Applied Mechanics and Materials 128-129 (October 2011): 504–9. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.504.

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In this paper, a singing evaluation approach based on pitch and rhythm accuracy is proposed. A comparative study was first carried out between speech and singing voice, which demonstrated the characteristic of consonant and vowel in singing voice. A phoneme boundary detector was adopted to perform note segmentation in a high time resolution by using characteristics of consonant and vowel. As for singing skill evaluation, relative pitch interval between reference score and actual sung pitch was adopted to estimate pitch deviation while difference between absolute position and expected position

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21

You, Shingchern D., Yi-Chung Wu, and Shih-Hsien Peng. "Comparative study of singing voice detection methods." Multimedia Tools and Applications 75, no. 23 (August 29, 2015): 15509–24. http://dx.doi.org/10.1007/s11042-015-2894-9.

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22

Foote, Jennifer R., Lauren P. Fitzsimmons, Lynnea M. Lobert, Laurene M. Ratcliffe, and Daniel J. Mennill. "A Population-level Analysis of Morning Song: Exploring the Implications for Point Counts." Canadian Field-Naturalist 131, no. 1 (July 14, 2017): 10–18. http://dx.doi.org/10.22621/cfn.v131i1.1779.

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Point counts are widely used for conducting ecological surveys of wild birds. Vocal output of birds varies with time of day, and therefore the results of ecological surveys should also vary with time of day. We modeled how males’ singing rates change over the morning. We calculated song rates in 3 min sampling periods (the standard sampling period used by the North American Breeding Bird Survey) and compared how detection rates vary as sampling period increases. We recorded singing activity in 15 neighbourhoods of breeding Black-capped Chickadees (Poecile atricapillus) with 16-element micropho

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23

O’Sullivan, Sean. "Bridges and gaps: The Singing Detective’s serial afterlife." Journal of Screenwriting 4, no. 3 (August 1, 2013): 273–85. http://dx.doi.org/10.1386/josc.4.3.273_1.

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24

Yu, Da-peng, De-you Zhao, and Yu Wang. "Detecting stationarity and nonlinearity in propeller singing signal." Journal of Shanghai Jiaotong University (Science) 15, no. 4 (August 2010): 447–50. http://dx.doi.org/10.1007/s12204-010-1031-1.

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25

Lech, Michał, and Bozena Kostek. "A system for automatic detection and correction of detuned singing." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3177. http://dx.doi.org/10.1121/1.2933266.

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26

Foote, Jennifer R., Mandy Ehnes, and Jeffrey P. Dech. "Seasonal changes in acoustic detection of forest birds." Journal of Ecoacoustics 2, no. 1 (April 11, 2018): 1. http://dx.doi.org/10.22261/jea.qvdzo7.

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Autonomous recording is commonly used to examine the structure of avian communities in a variety of landscapes. Many birds return to the breeding grounds in May yet acoustic surveys typically begin in June. In many species, singing activity declines through the breeding season and so detections may be lower later in the season. The aim of our study was to compare the species richness and the community composition measured early (mid-late May) and later (mid-late June) in the breeding season. We recorded the community of singing birds at 13 locations in York Region, Ontario, Canada woodlots ove

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27

Alldredge, Mathew W., Kenneth H. Pollock, Theodore R. Simons, Jaime A. Collazo, and Susan A. Shriner. "Time-of-Detection Method for Estimating Abundance From Point-Count Surveys." Auk 124, no. 2 (April 1, 2007): 653–64. http://dx.doi.org/10.1093/auk/124.2.653.

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Abstract Point-count surveys are often used to collect data on the abundance and distribution of birds, generally as an index of relative abundance. Valid comparison of these indices assumes that the detection process is comparable over space and time. These restrictive assumptions can be eliminated by estimating detection probabilities directly. We generalize a recently proposed removal model for estimating detection probabilities using a time-of-detection approach, which can account for more sources of variation in point-count data. This method is specifically designed to account for variati

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28

D’Amario, Sara, Helena Daffern, and Freya Bailes. "A new method of onset and offset detection in ensemble singing." Logopedics Phoniatrics Vocology 44, no. 4 (March 27, 2018): 143–58. http://dx.doi.org/10.1080/14015439.2018.1452977.

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29

Monson, Brian B., Andrew J. Lotto, and Brad H. Story. "Detection of high-frequency energy level changes in speech and singing." Journal of the Acoustical Society of America 135, no. 1 (January 2014): 400–406. http://dx.doi.org/10.1121/1.4829525.

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30

Michalska, Małgorzata. "Application for the Automatic Pitch Detection and Correction of Detuned Singing." Pomiary Automatyka Robotyka 20, no. 1 (March 2, 2016): 25–30. http://dx.doi.org/10.14313/par_219/25.

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31

Sheldon, Deborah A. "Effects of Contextual Sight-Singing and Aural Skills Training on Error-Detection Abilities." Journal of Research in Music Education 46, no. 3 (October 1998): 384–95. http://dx.doi.org/10.2307/3345550.

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32

Proutskova, Polina, Christophe Rhodes, Tim Crawford, and Geraint Wiggins. "Breathy, Resonant, Pressed – Automatic Detection of Phonation Mode from Audio Recordings of Singing." Journal of New Music Research 42, no. 2 (June 2013): 171–86. http://dx.doi.org/10.1080/09298215.2013.821496.

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33

Waymark, Peter. "John Caughie, Edge of Darkness (London: British Film Institute, 2007), pp. 153, ISBN 978 1 84457 20 7 (pb), £12. Glen Creeber, The Singing Detective (London: British Film Institute, 2007), pp. 154, ISBN 978 1 84457 198 7 (pb), £12. Glyn Davis, Queer as Folk (London: British Film Institute, 2007), pp. 138, ISBN 978 1 84457 199 4 (pb), £12." Journal of British Cinema and Television 5, no. 2 (November 2008): 425–27. http://dx.doi.org/10.3366/e1743452108000575.

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34

Pivnitskaya, Olga V. "The Peculiarities of Intonational Approach Realization within the Course of Folk Solfeggio Based on Russian Traditional Culture." Musical Art and Education 7, no. 1 (2019): 120–31. http://dx.doi.org/10.31862/2309-1428-2019-7-1-120-131.

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The given article touches upon reasonability and perspectivity of the development of intonation approach to the mastering of learning material at folk solfeggio lessons. As is known folk-oriented musical education has a long history in Russia. Meanwhile song folklore has always been an integral part of national and world music culture. During the historical revolution two trends in national ethnomusicology have developed. The first one is studied as a consistent logical system while the second one is connected with the detection of unique features of Russian folk music in different regions. Th

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35

Price, J. Jordan. "Why is birdsong so repetitive? Signal detection and the evolution of avian singing modes." Behaviour 150, no. 9-10 (2013): 995–1013. http://dx.doi.org/10.1163/1568539x-00003051.

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36

., Sumeet S. Andhalkar. "PITCH DETECTION FROM SINGING VOICE, ADVANTAGES, LIMITATIONS AND APPLICATIONS OF PITCH IN VOICE PROCESSING." International Journal of Research in Engineering and Technology 04, no. 04 (April 25, 2015): 616–19. http://dx.doi.org/10.15623/ijret.2015.0404106.

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37

Coath, Martin, Susan L. Denham, Leigh M. Smith, Henkjan Honing, Amaury Hazan, Piotr Holonowicz, and Hendrik Purwins. "Model cortical responses for the detection of perceptual onsets and beat tracking in singing." Connection Science 21, no. 2-3 (September 2009): 193–205. http://dx.doi.org/10.1080/09540090902733905.

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38

Lampe, Helene M., Torben Dabelsteen, Ole N. Larsen, and Simon B. Pedersen. "Degradation of song in a species using nesting holes: the Pied Flycatcher Ficedula hypoleuca." Anais da Academia Brasileira de Ciências 76, no. 2 (June 2004): 264–66. http://dx.doi.org/10.1590/s0001-37652004000200012.

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The habitat, but also the nest hole of a hole-nesting species, will degrade the song during transmission. We investigated how the sounds degrade in a sound transmission experiment with the song of the Pied Flycatcher Ficedula hypoleuca (Muscicapidae). Ten different song elements were transmitted to microphones placed inside and outside a nest box. On average, song degradation was much greater inside than outside the nest boxes, especially with respect to excess attenuation and blurring of the song elements. Being inside a nest box therefore strongly reduces a Pied Flycatcher's possibility of d

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39

Vatti, Marianna, Sébastien Santurette, Niels Henrik Pontoppidan, and Torsten Dau. "Perception of a Sung Vowel as a Function of Frequency-Modulation Rate and Excursion in Listeners With Normal Hearing and Hearing Impairment." Journal of Speech, Language, and Hearing Research 57, no. 5 (October 2014): 1961–71. http://dx.doi.org/10.1044/2014_jslhr-h-13-0219.

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Purpose Frequency fluctuations in human voices can usually be described as coherent frequency modulation (FM). As listeners with hearing impairment (HI listeners) are typically less sensitive to FM than listeners with normal hearing (NH listeners), this study investigated whether hearing loss affects the perception of a sung vowel based on FM cues. Method Vibrato maps were obtained in 14 NH and 12 HI listeners with different degrees of musical experience. The FM rate and FM excursion of a synthesized vowel, to which coherent FM was applied, were adjusted until a singing voice emerged. Results

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40

Gogala, Matija. "Acoustic detection and identification of singing cicadas (Homoptera: Cicadoidea) in Europe and in S.E. Asia." Journal of the Acoustical Society of America 106, no. 4 (October 1999): 2189. http://dx.doi.org/10.1121/1.427421.

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41

Nowosielska-Grygiel, Joanna, and Jurek Olszewski. "The usefulness of the acoustic and the capacity analysis of singing voice." Otolaryngologia Polska 73, no. 3 (April 5, 2019): 16–25. http://dx.doi.org/10.5604/01.3001.0013.1534.

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Abstract Introduction: The aim of the study was to assess the acoustic and capacity analysis of singing voice using DiagnoScope Specialist software. Material and methods: The study was conducted in 120 adults subjects, including 74 women and 46 men aged 21-5, were divided into 3 groups: I -40 subjects (treatment group) – professional vocalists, II- 40 subjects (treatment group) – semiprofessional vocalists, III- 40 subjects (control group) – students of The Military Medical Faculty at Medical University of Lodz – nonsingers. The research methodology included: primary medical history, physical

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42

Calladine, J., F. Buner, and N. J. Aebischer. "Temporal variations in the singing activity and the detection of Turtle DovesStreptopelia turtur: implications for surveys." Bird Study 46, no. 1 (March 1999): 74–80. http://dx.doi.org/10.1080/00063659909461116.

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43

Luther, David A. "Signaller: receiver coordination and the timing of communication in Amazonian birds." Biology Letters 4, no. 6 (September 30, 2008): 651–54. http://dx.doi.org/10.1098/rsbl.2008.0406.

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The efficacy of communication relies on the detection of signals against background noise. Some species are known to alter the timing of vocalizations to avoid acoustic interference from similar signals of other species, but nothing is known about the possibility of coordinated adjustments in the timing of receivers' attention. I examined the possibility that co-occurring species might respond as well as vocalize at different times in a diverse tropical avifauna by presenting playbacks of recordings to territorial birds at typical and atypical times for singing during the dawn chorus. The resu

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44

Hartbauer, Manfred, Elisabeth Ofner, Viktoria Grossauer, and Björn M. Siemers. "The Cercal Organ May Provide Singing Tettigoniids a Backup Sensory System for the Detection of Eavesdropping Bats." PLoS ONE 5, no. 9 (September 13, 2010): e12698. http://dx.doi.org/10.1371/journal.pone.0012698.

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45

Li, Feng, and Masato Akagi. "Blind monaural singing voice separation using rank-1 constraint robust principal component analysis and vocal activity detection." Neurocomputing 350 (July 2019): 44–52. http://dx.doi.org/10.1016/j.neucom.2019.04.030.

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46

Farnsworth, George L., Kenneth H. Pollock, James D. Nichols, Theodore R. Simons, James E. Hines, and John R. Sauer. "A Removal Model for Estimating Detection Probabilities From Point-Count Surveys." Auk 119, no. 2 (April 1, 2002): 414–25. http://dx.doi.org/10.1093/auk/119.2.414.

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AbstractUse of point-count surveys is a popular method for collecting data on abundance and distribution of birds. However, analyses of such data often ignore potential differences in detection probability. We adapted a removal model to directly estimate detection probability during point-count surveys. The model assumes that singing frequency is a major factor influencing probability of detection when birds are surveyed using point counts. This may be appropriate for surveys in which most detections are by sound. The model requires counts to be divided into several time intervals. Point count

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47

Hiçyılmaz, Yaşar, and Stefano Moretti. "Characterisation of dark matter in direct detection experiments: Singlino versus Higgsino." Nuclear Physics B 967 (June 2021): 115404. http://dx.doi.org/10.1016/j.nuclphysb.2021.115404.

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48

Hao, Yiya, Yaobin Chen, Weiwei Zhang, Gong Chen, and Liang Ruan. "A real-time music detection method based on convolutional neural network using Mel-spectrogram and spectral flux." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 1 (August 1, 2021): 5910–18. http://dx.doi.org/10.3397/in-2021-11599.

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Audio processing, including speech enhancement system, improves speech intelligibility and quality in real-time communication (RTC) such as online meetings and online education. However, such processing, primarily noise suppression and automatic gain control, is harmful to music quality when the captured signal is music instead of speech. A music detector can solve the issue above by switching off the speech processing when the music is detected. In RTC scenarios, the music detector should be low-complexity and cover various situations, including different types of music, background noises, an

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49

McNeil, Darin J., and Christina M. Grozinger. "Singing in the suburbs: point count surveys efficiently reveal habitat associations for nocturnal Orthoptera across an urban-to-rural gradient." Journal of Insect Conservation 24, no. 6 (September 29, 2020): 1031–43. http://dx.doi.org/10.1007/s10841-020-00273-9.

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Abstract As evidence for global insect population declines continues to amass, several studies have indicated that Orthoptera (grasshoppers, crickets, and katydids) are among the most threatened insect groups. Understanding Orthoptera populations across large spatial extents requires efficient survey protocols, however, many previously established methods are expensive and/or labor-intensive. One survey method widely employed in wildlife biology, the aural point count, may work well for crickets and katydids (suborder: Ensifera) because males produce conspicuous, species-specific mating calls.

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50

KERKHOFF, ANNEMARIE, ELISE DE BREE, MAARTJE DE KLERK, and FRANK WIJNEN. "Non-adjacent dependency learning in infants at familial risk of dyslexia." Journal of Child Language 40, no. 1 (December 5, 2012): 11–28. http://dx.doi.org/10.1017/s0305000912000098.

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ABSTRACTThis study tests the hypothesis that developmental dyslexia is (partly) caused by a deficit in implicit sequential learning, by investigating whether infants at familial risk of dyslexia can track non-adjacent dependencies in an artificial language. An implicit learning deficit would hinder detection of such dependencies, which mark grammatical relations (e.g. between ‘is’ and ‘-ing’ in ‘she is happily singing’). In a head-turn experiment with infants aged 1;6, family risk and typically developing infants were exposed to one of two novel languages containing dependencies of the type a-

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