Revolt music conference 2017

Author: f | 2025-04-23

★★★★☆ (4.7 / 1484 reviews)

itunes big sur

Be at the Revolt Music Conference: Sync Discovery - Revolt Music Conference 2025 (Upcoming Artists) Download and install REVOLT Music Conference 2025 v.4 on Windows PC. The REVOLT Music Conference is a convergence for all who thrive in the music and

windows movie maker windows 7

Revolt Music Conference - projectlevel.org

T-minus 6 days until the 4th Annual REVOLT Music Conference hits South Beach!This year we’re headed to Miami, FL (Oct. 12-15), to be in the building with some of the music industry’s finest as the #1 name in music honors rapper, singer, actor, and mogul, Queen Latifah, with the REVOLT Icon Award.At the Gala honoring the Queen on October 14, RMC 2017 will host performances by Ms. Lauryn Hill, Daniel Caesar, and 2017’s breakout star –for those of you that were sleeping on her– SZA!“RMC is known for uniting successful, innovative, and thought-provoking speakers and industry executives from across the music and entertainment industry – all in one place,” says Andre Harrell, REVOLT Vice Chairman and Chair of the REVOLT Music Conference. “We are taking it to the next level and our 4th year is going to be the biggest RMC yet.”The Conference kicks off on October 12 with a Bad Boy performance featuring the boss man himself, Diddy, French Montana, 21 Savage, and King Combs. Following that, on October 13, 2 Chainz will host an exclusive yacht party along with a few of his friends. Then all the festivities close out with the Beyond the Lens Film Festival that will award one lucky winner the title of “Best Young Filmmaker.”There’s still time to get your tickets for REVOLT’s biggest Music Conference yet by clicking here. And if you need anymore convincing about why you should go, check out some highlights from last year’s Conference below. 2009. In Proceedings of the ISMIR—10th International Society for Music Information Retrieval Conference, Utrecht, The Netherlands, 26–30 October 2009; p. 1. [Google Scholar]Klapuri, A.P. A perceptually motivated multiple-f0 estimation method. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, 16–19 October 2005; pp. 291–294. [Google Scholar]Cañadas Quesada, F.J.; Ruiz Reyes, N.; Vera Candeas, P.; Carabias, J.J.; Maldonado, S. A multiple-F0 estimation approach based on Gaussian spectral modelling for polyphonic music transcription. J. New Music. Res. 2010, 39, 93–107. [Google Scholar] [CrossRef]Chang, W.C.; Su, A.W.; Yeh, C.; Roebel, A.; Rodet, X. Multiple-F0 tracking based on a high-order HMM model. In Digital Audio Effects (DAFx-08); HAL: Bangalore, India, 2008. [Google Scholar]Christensen, M.; Jakobsson, A. Multi-Pitch Estimation; Synthesis Lectures on Speech and Audio Processing Series; Morgan and Claypool: San Rafael, CA, USA, 2009. [Google Scholar]Cuesta, H.; McFee, B.; Gómez, E. Multiple f0 estimation in vocal ensembles using convolutional neural networks. In Proceedings of the ISMIR—21th International Society for Music Information Retrieval Conference, virtual conference, 11–16 October 2020; pp. 302–309. [Google Scholar]Yang, L.; Maezawa, A.; Smith, J.B.; Chew, E. Probabilistic transcription of sung melody using a pitch dynamic model. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orlean, LA, USA, 5–9 March 2017; pp. 301–305. [Google Scholar]Meseguer-Brocal, G.; Bittner, R.; Durand, S.; Brost, B. Data cleansing with contrastive learning for vocal note event annotations. arXiv 2020, arXiv:2008.02069. [Google Scholar]Molina, E.; Tardon, L.J.; Barbancho, A.M.; Barbancho, I.

Caesarstone at Revolt Music Conference

SiPTH: Singing Transcription Based on Hysteresis Defined on the Pitch-Time Curve. IEEE/ACM Trans. Audio Speech Lang. Process. 2014, 23, 252–263. [Google Scholar] [CrossRef]Bittner, R.M.; Pasalo, K.; Bosch, J.J.; Meseguer-Brocal, G.; Rubinstein, D. vocadito: A dataset of solo vocals with f0, note, and lyric annotations. arXiv 2021, arXiv:2110.05580. [Google Scholar]Ternström, S. Choir acoustics: An overview of scientific research published to date. Int. J. Res. Choral Sing. 2003, 1, 3–12. [Google Scholar]Dai, J.; Dixon, S. Analysis of Interactive Intonation in Unaccompanied SATB Ensembles. In Proceedings of the ISMIR—18th International Society for Music Information Retrieval Conference, Suzhou, China, 23–27 October 2017; pp. 599–605. [Google Scholar]Schramm, R.; Benetos, E. Automatic Transcription of a Cappella Recordings from Multiple Singers. In Proceedings of the Audio Engineering Society Conference, Arlington, VA, USA, 15–17 June 2017; Available online: (accessed on 5 October 2023).Ternström, S. Perceptual evaluations of voice scatter in unison choir sounds. J. Voice 1993, 7, 129–135. [Google Scholar] [CrossRef] [PubMed]McLeod, A.; Schramm, R.; Steedman, M.; Benetos, E. Automatic Transcription of Polyphonic Vocal Music. Appl. Sci. 2017, 7, 1285. [Google Scholar] [CrossRef]Schramm, R. Automatic transcription of polyphonic vocal music. In Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity; Springer: Berlin/Heidelberg, Germany, 2021; pp. 715–735. [Google Scholar]Hawthorne, C.; Elsen, E.; Song, J.; Roberts, A.; Simon, I.; Raffel, C.; Engel, J.; Oore, S.; Eck, D. Onsets and frames: Dual-objective piano transcription. arXiv 2017, arXiv:1710.11153. [Google Scholar]Kong, Q.; Li, B.; Song, X.; Wan, Y.; Wang, Y. High-Resolution Piano Transcription with Pedals by Regressing Onset and. Be at the Revolt Music Conference: Sync Discovery - Revolt Music Conference 2025 (Upcoming Artists)

Revolt Music Conference 2025 Setlists

Spain, 10–14 October 2004. [Google Scholar]Bittner, R.M.; McFee, B.; Salamon, J.; Li, P.; Bello, J.P. Deep Salience Representations for F0 Estimation in Polyphonic Music. In Proceedings of the ISMIR—International Society for Music Information Retrieval Conference, Suzhou, China, 23–27 October 2017; pp. 63–70. [Google Scholar]Salamon, J.; Gomez, E.; Ellis, D.P.W.; Richard, G. Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges. IEEE Signal Process. Mag. 2014, 31, 118–134. [Google Scholar] [CrossRef]Salamon, J.; Urbano, J. Current Challenges in the Evaluation of Predominant Melody Extraction Algorithms. In Proceedings of the ISMIR–13th International Society for Music Information Retrieval Conference, Porto, Portugal, 8–12 October 2012; Volume 12, pp. 289–294. [Google Scholar]Gao, Y.; Zhang, X.; Li, W. Vocal Melody Extraction via HRNet-Based Singing Voice Separation and Encoder-Decoder-Based F0 Estimation. Electronics 2021, 10, 298. [Google Scholar] [CrossRef]Lu, W.T.; Su, L. Vocal Melody Extraction with Semantic Segmentation and Audio-symbolic Domain Transfer Learning. In Proceedings of the ISMIR—19th International Society for Music Information Retrieval Conference, Paris, France, 23–27 September 2018; pp. 521–528. [Google Scholar]Su, L. Vocal melody extraction using patch-based CNN. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, AB, Canada, 15–20 April 2018; pp. 371–375. [Google Scholar]Kum, S.; Lin, J.H.; Su, L.; Nam, J. Semi-supervised learning using teacher-student models for vocal melody extraction. arXiv 2020, arXiv:2008.06358. [Google Scholar]Kum, S.; Nam, J. Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks. Appl. Sci. 2019, 9, 1324. [Google Scholar] [CrossRef]Yeh, C.; Roebel, A. Multiple-F0 Estimation for Mirex Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training. arXiv 2023, arXiv:2302.00286. [Google Scholar]Manilow, E.; Seetharaman, P.; Pardo, B. Simultaneous separation and transcription of mixtures with multiple polyphonic and percussive instruments. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), Barcelona, Spain, 4–8 May 2020; pp. 771–775. [Google Scholar]Yamamoto, Y.; Nam, J.; Terasawa, H. PrimaDNN’: A Characteristics-aware DNN Customization for Singing Technique Detection. European signal processing conference (EUSIPCO). arXiv 2023, arXiv:2306.14191. [Google Scholar]Yamamoto, Y.; Nam, J.; Terasawa, H. Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers. In Proceedings of the ISMIR—23th International Society for Music Information Retrieval Conference, Bengaluru, India, 4–8 December 2022. [Google Scholar]Heidemann, K.A. System for Describing Vocal Timbre in Popular Song. J. Soc. Music Theory 2016, 22, 1–17. [Google Scholar] [CrossRef]Emmons, S.; Chase, C. Prescriptions for Choral Excellence; Oxford University Press: Oxford, UK, 2006. [Google Scholar]Doscher, B.M. The Functional Unity of the Singing Voice; Scarecrow Press: Lanham, MD, USA, 1993. [Google Scholar]Kanno, M. Thoughts on how to play in tune: Pitch and intonation. Contemp. Music. Rev. 2003, 22, 35–52. [Google Scholar] [CrossRef]Huang, H.; Huang, R. She Sang as She Spoke: Billie Holiday and Aspects of Speech Intonation and Diction. Jazz Perspect. 2013, 7, 287–302. [Google Scholar] [CrossRef]McAdams, S.; Goodchild, M. Musical structure: Sound and timbre. In Routledge Companion to Music Cognition; Routledge: London, UK, 2017; pp. 129–139. [Google Scholar]Xiao, Z.; Chen, X.; Zhou, L. Polyphonic piano transcription based on graph convolutional network. Signal Process.

2025 Revolt Music Conference - Facebook

2023, 212, 109134. [Google Scholar] [CrossRef]Yang, D.; Tsai, T.J. Piano sheet music identification using dynamic n-gram fingerprinting. Trans. Int. Soc. Music. Inf. Retr. 2021, 1, 4. [Google Scholar] [CrossRef]Hasanain, A.; Syed, M.; Kepuska, V.; Silaghi, M. Multi-Dimensional Spectral Process for Cepstral Feature Engineering & Formant Coding. J. Electr. Electron. Eng. 2022, 1, 1–20. [Google Scholar]Korzeniowski, F.; Widmer, G. Feature learning for chord recognition: The deep chroma extractor. arXiv 2016, arXiv:1612.05065. [Google Scholar]Repp, B.H.; Luke Windsor, W.; Desain, P. Effects of tempo on the timing of simple musical rhythms. Music. Percept. 2002, 19, 565–593. [Google Scholar] [CrossRef]Quinn, S.; Watt, R. The perception of tempo in music. Perception 2006, 35, 267–280. [Google Scholar] [CrossRef]Koops, H.V.; de Haas, W.B.; Burgoyne, J.A.; Bransen, J.; Kent-Muller, A.; Volk, A. Annotator subjectivity in harmony annotations of popular music. J. New Music. Res. 2019, 48, 232–252. [Google Scholar] [CrossRef]Kurth, F.; Müller, M.; Fremerey, C.; Chang, Y.H.; Clausen, M. Automated Synchronization of Scanned Sheet Music with Audio Recordings. In Proceedings of the ISMIR—8th International Society for Music Information Retrieval Conference, Vienna, Austria, 23–27 September 2007; pp. 261–266. [Google Scholar]Bereket, M.; Shi, K. An AI Approach to AutZomatic Natural Music Transcription; Stanford University: Stanford, CA, USA, 2017. [Google Scholar]Doremir Music Research AB, ScoreCloud. Available online: (accessed on 5 October 2023).Audacity Software. 1999. Available online: (accessed on 5 October 2023).McFee, B.; Raffel, C.; Liang, D.; Ellis, D.P.; McVicar, M.; Battenberg, E.; Nieto, O. librosa: Audio and music signal analysis in python. In Proceedings of the 14th Python in Science Conference

REVOLT Music Conference 2025 by REVOLT Media and TV, LLC

Scholar]Gong, X.; Xu, W.; Liu, J.; Cheng, W. Analysis and correction of maps dataset. In Proceedings of the 22th International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, 2–6 September 2019. [Google Scholar]Manilow, E.; Wichern, G.; Seetharaman, P.; Le Roux, J. Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity. In Proceedings of the Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2019), New Paltz, NY, USA, 20–23 October 2019; pp. 45–49. [Google Scholar]Thickstun, J.; Harchaoui, Z.; Kakade, S. Learning features of music from scratch. arXiv 2017, arXiv:1611.09827. [Google Scholar]Li, B.; Liu, X.; Dinesh, K.; Duan, Z.; Sharma, G. Creating a Multitrack Classical Music Performance Dataset for Multimodal Music Analysis: Challenges, Insights, and Applications. IEEE Trans. Multimed. 2018, 21, 522–535. [Google Scholar] [CrossRef]Ou, L.; Guo, Z.; Benetos, E.; Han, J.; Wang, Y. Exploring transformer’s potential on automatic piano transcription. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), Singapore, 22–27 May 2022; pp. 776–780. [Google Scholar] Figure 1. An example for the illustration of four different levels of music transcription. Figure 1. An example for the illustration of four different levels of music transcription. Table 1. The description of currently available music transcription datasets. Table 1. The description of currently available music transcription datasets. Dataset NameProposeDescriptionMIR-1K [117]Vocal MelodyThe MIR-1K dataset offers a diverse collection of music excerpts spanning various genres, with detailed annotations of pitch contours in semitones, indices, and types of. Be at the Revolt Music Conference: Sync Discovery - Revolt Music Conference 2025 (Upcoming Artists) Download and install REVOLT Music Conference 2025 v.4 on Windows PC. The REVOLT Music Conference is a convergence for all who thrive in the music and

Revolt Music Conference 2025 Performers - YouTube

Patrick) (Entered: 11/09/2017) 7 Nov 28, 2017 NOTICE of Appearance by Mercedes Colwin on behalf of All Defendants (aty to be noticed) (Colwin, Mercedes) (Entered: 11/28/2017) 8 Nov 28, 2017 NOTICE of Appearance by David J. Grech on behalf of All Defendants (aty to be noticed) (Grech, David) (Entered: 11/28/2017) 9 Nov 28, 2017 NOTICE of Appearance by Francis James Giambalvo on behalf of All Defendants (aty to be noticed) (Giambalvo, Francis) (Entered: 11/28/2017) 10 Dec 8, 2017 WAIVER OF SERVICE Returned Executed by Tamilla Mukhailova. All Defendants. (Attachments: # 1 Waiver of Service Rider) (Wittels, Steven) (Entered: 12/08/2017) 11 Dec 16, 2017 Joint MOTION to Adjourn Conference Scheduled for 12/20 (addressed to Judge Tomlinson) by Tamilla Mukhailova. (Wittels, Steven) (Entered: 12/16/2017) Dec 18, 2017 ORDER granting 11 Motion to Adjourn Conference. The Initial Conference is adjourned to January 23, 2018 at 11:30 a.m. The parties shall file their joint proposed discovery plan by January 19, 2018. Ordered by Magistrate Judge A. Kathleen Tomlinson on 12/18/2017. (Cento, Patrick) Dec 18, 2017 Order on Motion to Adjourn Conference 12 Jan 9, 2018 NOTICE of Change of Firm, Address, E-mail by Andrey Belenky (Belenky, Andrey) (Entered: 01/09/2018) 13 Jan 16, 2018 Letter MOTION for pre motion conference concerning Defendants' intented Motion to Dismiss the Complaint by Matthew Bryant, FBA of Syosset LLC, John Kuveikis, Viktor Shick, Bill Squires. (Giambalvo, Francis) (Entered: 01/16/2018) Jan 17, 2018 ORDER granting 13 Motion for Pre Motion Conference. By letter dated January 16, 2018, defendants request a pre-motion conference in anticipation of filing a motion to dismiss. IT IS HEREBY ORDERED that the parties shall participate in a pre-motion conference with the Court on Tuesday, January 23, 2018, at 11:00 a.m. Prior to the date of the conference, plaintiff may submit a letter pursuant to Individual Rule III.A explaining why the proposed motion is likely to be unsuccessful. SO ORDERED. Ordered by Judge Joseph F. Bianco on 1/17/2018. (Kuhn, Alyssa) 14 Jan 19, 2018 Proposed Scheduling Order Joint Proposed Discovery Plan by Tamilla Mukhailova (Wittels, Steven) (Entered: 01/19/2018) Jan 22, 2018 SCHEDULING ORDER: Due to a conflict in the Court's calendar, IT IS HEREBY ORDERED that the pre-motion conference scheduled for tomorrow, Tuesday, January 23, 2018, at 11:00 a.m. is rescheduled as a telephone conference for Monday, January 29, 2018, at 2:45 p.m. At that time, counsel for defendants shall initiate the call and, once all parties are on the line, shall contact Chambers at (631) 712-5670. SO ORDERED. Ordered by Judge Joseph F. Bianco on 1/22/2018. (Kuhn, Alyssa) 15 Jan 23, 2018 Minute Entry for proceedings held before Magistrate Judge A. Kathleen Tomlinson: Initial Conference Hearing held on 1/23/2018. Counsel are directed to contact Chambers to

Comments

User2790

T-minus 6 days until the 4th Annual REVOLT Music Conference hits South Beach!This year we’re headed to Miami, FL (Oct. 12-15), to be in the building with some of the music industry’s finest as the #1 name in music honors rapper, singer, actor, and mogul, Queen Latifah, with the REVOLT Icon Award.At the Gala honoring the Queen on October 14, RMC 2017 will host performances by Ms. Lauryn Hill, Daniel Caesar, and 2017’s breakout star –for those of you that were sleeping on her– SZA!“RMC is known for uniting successful, innovative, and thought-provoking speakers and industry executives from across the music and entertainment industry – all in one place,” says Andre Harrell, REVOLT Vice Chairman and Chair of the REVOLT Music Conference. “We are taking it to the next level and our 4th year is going to be the biggest RMC yet.”The Conference kicks off on October 12 with a Bad Boy performance featuring the boss man himself, Diddy, French Montana, 21 Savage, and King Combs. Following that, on October 13, 2 Chainz will host an exclusive yacht party along with a few of his friends. Then all the festivities close out with the Beyond the Lens Film Festival that will award one lucky winner the title of “Best Young Filmmaker.”There’s still time to get your tickets for REVOLT’s biggest Music Conference yet by clicking here. And if you need anymore convincing about why you should go, check out some highlights from last year’s Conference below.

2025-04-09
User6359

2009. In Proceedings of the ISMIR—10th International Society for Music Information Retrieval Conference, Utrecht, The Netherlands, 26–30 October 2009; p. 1. [Google Scholar]Klapuri, A.P. A perceptually motivated multiple-f0 estimation method. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, 16–19 October 2005; pp. 291–294. [Google Scholar]Cañadas Quesada, F.J.; Ruiz Reyes, N.; Vera Candeas, P.; Carabias, J.J.; Maldonado, S. A multiple-F0 estimation approach based on Gaussian spectral modelling for polyphonic music transcription. J. New Music. Res. 2010, 39, 93–107. [Google Scholar] [CrossRef]Chang, W.C.; Su, A.W.; Yeh, C.; Roebel, A.; Rodet, X. Multiple-F0 tracking based on a high-order HMM model. In Digital Audio Effects (DAFx-08); HAL: Bangalore, India, 2008. [Google Scholar]Christensen, M.; Jakobsson, A. Multi-Pitch Estimation; Synthesis Lectures on Speech and Audio Processing Series; Morgan and Claypool: San Rafael, CA, USA, 2009. [Google Scholar]Cuesta, H.; McFee, B.; Gómez, E. Multiple f0 estimation in vocal ensembles using convolutional neural networks. In Proceedings of the ISMIR—21th International Society for Music Information Retrieval Conference, virtual conference, 11–16 October 2020; pp. 302–309. [Google Scholar]Yang, L.; Maezawa, A.; Smith, J.B.; Chew, E. Probabilistic transcription of sung melody using a pitch dynamic model. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orlean, LA, USA, 5–9 March 2017; pp. 301–305. [Google Scholar]Meseguer-Brocal, G.; Bittner, R.; Durand, S.; Brost, B. Data cleansing with contrastive learning for vocal note event annotations. arXiv 2020, arXiv:2008.02069. [Google Scholar]Molina, E.; Tardon, L.J.; Barbancho, A.M.; Barbancho, I.

2025-03-28
User4754

SiPTH: Singing Transcription Based on Hysteresis Defined on the Pitch-Time Curve. IEEE/ACM Trans. Audio Speech Lang. Process. 2014, 23, 252–263. [Google Scholar] [CrossRef]Bittner, R.M.; Pasalo, K.; Bosch, J.J.; Meseguer-Brocal, G.; Rubinstein, D. vocadito: A dataset of solo vocals with f0, note, and lyric annotations. arXiv 2021, arXiv:2110.05580. [Google Scholar]Ternström, S. Choir acoustics: An overview of scientific research published to date. Int. J. Res. Choral Sing. 2003, 1, 3–12. [Google Scholar]Dai, J.; Dixon, S. Analysis of Interactive Intonation in Unaccompanied SATB Ensembles. In Proceedings of the ISMIR—18th International Society for Music Information Retrieval Conference, Suzhou, China, 23–27 October 2017; pp. 599–605. [Google Scholar]Schramm, R.; Benetos, E. Automatic Transcription of a Cappella Recordings from Multiple Singers. In Proceedings of the Audio Engineering Society Conference, Arlington, VA, USA, 15–17 June 2017; Available online: (accessed on 5 October 2023).Ternström, S. Perceptual evaluations of voice scatter in unison choir sounds. J. Voice 1993, 7, 129–135. [Google Scholar] [CrossRef] [PubMed]McLeod, A.; Schramm, R.; Steedman, M.; Benetos, E. Automatic Transcription of Polyphonic Vocal Music. Appl. Sci. 2017, 7, 1285. [Google Scholar] [CrossRef]Schramm, R. Automatic transcription of polyphonic vocal music. In Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity; Springer: Berlin/Heidelberg, Germany, 2021; pp. 715–735. [Google Scholar]Hawthorne, C.; Elsen, E.; Song, J.; Roberts, A.; Simon, I.; Raffel, C.; Engel, J.; Oore, S.; Eck, D. Onsets and frames: Dual-objective piano transcription. arXiv 2017, arXiv:1710.11153. [Google Scholar]Kong, Q.; Li, B.; Song, X.; Wan, Y.; Wang, Y. High-Resolution Piano Transcription with Pedals by Regressing Onset and

2025-04-11
User9660

Spain, 10–14 October 2004. [Google Scholar]Bittner, R.M.; McFee, B.; Salamon, J.; Li, P.; Bello, J.P. Deep Salience Representations for F0 Estimation in Polyphonic Music. In Proceedings of the ISMIR—International Society for Music Information Retrieval Conference, Suzhou, China, 23–27 October 2017; pp. 63–70. [Google Scholar]Salamon, J.; Gomez, E.; Ellis, D.P.W.; Richard, G. Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges. IEEE Signal Process. Mag. 2014, 31, 118–134. [Google Scholar] [CrossRef]Salamon, J.; Urbano, J. Current Challenges in the Evaluation of Predominant Melody Extraction Algorithms. In Proceedings of the ISMIR–13th International Society for Music Information Retrieval Conference, Porto, Portugal, 8–12 October 2012; Volume 12, pp. 289–294. [Google Scholar]Gao, Y.; Zhang, X.; Li, W. Vocal Melody Extraction via HRNet-Based Singing Voice Separation and Encoder-Decoder-Based F0 Estimation. Electronics 2021, 10, 298. [Google Scholar] [CrossRef]Lu, W.T.; Su, L. Vocal Melody Extraction with Semantic Segmentation and Audio-symbolic Domain Transfer Learning. In Proceedings of the ISMIR—19th International Society for Music Information Retrieval Conference, Paris, France, 23–27 September 2018; pp. 521–528. [Google Scholar]Su, L. Vocal melody extraction using patch-based CNN. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, AB, Canada, 15–20 April 2018; pp. 371–375. [Google Scholar]Kum, S.; Lin, J.H.; Su, L.; Nam, J. Semi-supervised learning using teacher-student models for vocal melody extraction. arXiv 2020, arXiv:2008.06358. [Google Scholar]Kum, S.; Nam, J. Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks. Appl. Sci. 2019, 9, 1324. [Google Scholar] [CrossRef]Yeh, C.; Roebel, A. Multiple-F0 Estimation for Mirex

2025-04-14
User1132

Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training. arXiv 2023, arXiv:2302.00286. [Google Scholar]Manilow, E.; Seetharaman, P.; Pardo, B. Simultaneous separation and transcription of mixtures with multiple polyphonic and percussive instruments. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), Barcelona, Spain, 4–8 May 2020; pp. 771–775. [Google Scholar]Yamamoto, Y.; Nam, J.; Terasawa, H. PrimaDNN’: A Characteristics-aware DNN Customization for Singing Technique Detection. European signal processing conference (EUSIPCO). arXiv 2023, arXiv:2306.14191. [Google Scholar]Yamamoto, Y.; Nam, J.; Terasawa, H. Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers. In Proceedings of the ISMIR—23th International Society for Music Information Retrieval Conference, Bengaluru, India, 4–8 December 2022. [Google Scholar]Heidemann, K.A. System for Describing Vocal Timbre in Popular Song. J. Soc. Music Theory 2016, 22, 1–17. [Google Scholar] [CrossRef]Emmons, S.; Chase, C. Prescriptions for Choral Excellence; Oxford University Press: Oxford, UK, 2006. [Google Scholar]Doscher, B.M. The Functional Unity of the Singing Voice; Scarecrow Press: Lanham, MD, USA, 1993. [Google Scholar]Kanno, M. Thoughts on how to play in tune: Pitch and intonation. Contemp. Music. Rev. 2003, 22, 35–52. [Google Scholar] [CrossRef]Huang, H.; Huang, R. She Sang as She Spoke: Billie Holiday and Aspects of Speech Intonation and Diction. Jazz Perspect. 2013, 7, 287–302. [Google Scholar] [CrossRef]McAdams, S.; Goodchild, M. Musical structure: Sound and timbre. In Routledge Companion to Music Cognition; Routledge: London, UK, 2017; pp. 129–139. [Google Scholar]Xiao, Z.; Chen, X.; Zhou, L. Polyphonic piano transcription based on graph convolutional network. Signal Process.

2025-04-07

Add Comment