publication . Preprint . Conference object . 2018

Extended playing techniques: The next milestone in musical instrument recognition

Lostanlen, Vincent; Andén, Joakim; Lagrange, Mathieu;
Open Access English
  • Published: 28 Sep 2018
  • Country: France
Abstract
Comment: 10 pages, 9 figures. The source code to reproduce the experiments of this paper is made available at: https://www.github.com/mathieulagrange/dlfm2018
Subjects
free text keywords: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, playing technique similarity, musical instrument recognition, scat- tering transform, metric learning, large-margin nearest neighbors, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Funded by
EC| INVARIANTCLASS
Project
INVARIANTCLASS
Invariant Representations for High-Dimensional Signal Classifications
  • Funder: European Commission (EC)
  • Project Code: 320959
  • Funding stream: FP7 | SP2 | ERC
64 references, page 1 of 5

[1] Joakim Ande´n, Vincent Lostanlen, and Ste´phane Mallat. 2018. Classi€cation with Joint Time-Frequency ScaŠering. (Jul 2018). arXiv:1807.08869

[2] Joakim Ande´n and Ste´phane Mallat. 2012. ScaŠering representation of modulated sounds. In Proc. DAFx.

[3] Joakim Ande´n and Ste´phane Mallat. 2014. Deep scaŠering spectrum. IEEE Trans. Sig. Proc. 62, 16 (2014), 4114-4128.

[4] Aure´lien Antoine and Eduardo R. Miranda. 2018. Musical Acoustics, Timbre, and Computer-Aided Orchestration Challenges. In Proc. ISMA.

[5] Aure´lien Bellet, Amaury Habrard, and Marc Sebban. 2013. A survey on metric learning for feature vectors and structured data. (2013). arXiv:1306.6709 [OpenAIRE]

[6] Serge Belongie and Pietro Perona. 2016. Visipedia circa 2015. Paˆern Recognition Leˆers 72 (2016), 15-24.

[7] Emmanouil Benetos, Margarita KoŠi, and Constantine Kotropoulos. 2006. Musical instrument classi€cation using non-negative matrix factorization algorithms and subset feature selection. In Proc. IEEE ICASSP. [OpenAIRE]

[8] Michel Bernays and Caroline Traube. 2013. Expressive production of piano timbre: touch and playing techniques for timbre control in piano performance. In Proc. SMC. [OpenAIRE]

[9] D.G. Bhalke, C.B. Rama Rao, and DaŠatraya S. Bormane. 2016. Automatic musical instrument classi€cation using fractional Fourier transform based-MFCC features and counter propagation neural network. J. Intell. Inf. Syst. 46, 3 (2016), 425-446.

[10] Rachel M. BiŠner, Justin Salamon, Mike Tierney, MaŠhias Mauch, Chris Cannam, and Juan Pablo Bello. 2014. MedleyDB: A multitrack dataset for annotationintensive MIR research. In Proc. ISMIR.

[11] Dimitry Bogdanov, Alastair Porter, Perfecto Herrera Boyer, and Xavier Serra. 2016. Cross-collection evaluation for music classi€cation tasks. In Proc. ISMIR.

[12] Judith C. Brown. 1999. Computer identi€cation of musical instruments using paŠern recognition with cepstral coecients as features. J. Acoust. Soc. Am. 105, 3 (1999), 1933-1941.

[13] Juan Jose´ Burred, Axel Robel, and Œomas Sikora. 2009. Polyphonic musical instrument recognition based on a dynamic model of the spectral envelope. In Proc. IEEE ICASSP. 173-176.

[14] Yuan-Ping Chen, Li Su, and Yi-Hsuan Yang. 2015. Electric Guitar Playing Technique Detection in Real-World Recording Based on F0 Sequence PaŠern Recognition. In Proc. ISMIR.

[15] Taishih Chi, Powen Ru, and Shihab A. Shamma. 2005. Multiresolution spectrotemporal analysis of complex sounds. J. Acoust. Soc. Am. 118, 2 (2005), 887-906. [OpenAIRE]

64 references, page 1 of 5
Abstract
Comment: 10 pages, 9 figures. The source code to reproduce the experiments of this paper is made available at: https://www.github.com/mathieulagrange/dlfm2018
Subjects
free text keywords: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, playing technique similarity, musical instrument recognition, scat- tering transform, metric learning, large-margin nearest neighbors, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Funded by
EC| INVARIANTCLASS
Project
INVARIANTCLASS
Invariant Representations for High-Dimensional Signal Classifications
  • Funder: European Commission (EC)
  • Project Code: 320959
  • Funding stream: FP7 | SP2 | ERC
64 references, page 1 of 5

[1] Joakim Ande´n, Vincent Lostanlen, and Ste´phane Mallat. 2018. Classi€cation with Joint Time-Frequency ScaŠering. (Jul 2018). arXiv:1807.08869

[2] Joakim Ande´n and Ste´phane Mallat. 2012. ScaŠering representation of modulated sounds. In Proc. DAFx.

[3] Joakim Ande´n and Ste´phane Mallat. 2014. Deep scaŠering spectrum. IEEE Trans. Sig. Proc. 62, 16 (2014), 4114-4128.

[4] Aure´lien Antoine and Eduardo R. Miranda. 2018. Musical Acoustics, Timbre, and Computer-Aided Orchestration Challenges. In Proc. ISMA.

[5] Aure´lien Bellet, Amaury Habrard, and Marc Sebban. 2013. A survey on metric learning for feature vectors and structured data. (2013). arXiv:1306.6709 [OpenAIRE]

[6] Serge Belongie and Pietro Perona. 2016. Visipedia circa 2015. Paˆern Recognition Leˆers 72 (2016), 15-24.

[7] Emmanouil Benetos, Margarita KoŠi, and Constantine Kotropoulos. 2006. Musical instrument classi€cation using non-negative matrix factorization algorithms and subset feature selection. In Proc. IEEE ICASSP. [OpenAIRE]

[8] Michel Bernays and Caroline Traube. 2013. Expressive production of piano timbre: touch and playing techniques for timbre control in piano performance. In Proc. SMC. [OpenAIRE]

[9] D.G. Bhalke, C.B. Rama Rao, and DaŠatraya S. Bormane. 2016. Automatic musical instrument classi€cation using fractional Fourier transform based-MFCC features and counter propagation neural network. J. Intell. Inf. Syst. 46, 3 (2016), 425-446.

[10] Rachel M. BiŠner, Justin Salamon, Mike Tierney, MaŠhias Mauch, Chris Cannam, and Juan Pablo Bello. 2014. MedleyDB: A multitrack dataset for annotationintensive MIR research. In Proc. ISMIR.

[11] Dimitry Bogdanov, Alastair Porter, Perfecto Herrera Boyer, and Xavier Serra. 2016. Cross-collection evaluation for music classi€cation tasks. In Proc. ISMIR.

[12] Judith C. Brown. 1999. Computer identi€cation of musical instruments using paŠern recognition with cepstral coecients as features. J. Acoust. Soc. Am. 105, 3 (1999), 1933-1941.

[13] Juan Jose´ Burred, Axel Robel, and Œomas Sikora. 2009. Polyphonic musical instrument recognition based on a dynamic model of the spectral envelope. In Proc. IEEE ICASSP. 173-176.

[14] Yuan-Ping Chen, Li Su, and Yi-Hsuan Yang. 2015. Electric Guitar Playing Technique Detection in Real-World Recording Based on F0 Sequence PaŠern Recognition. In Proc. ISMIR.

[15] Taishih Chi, Powen Ru, and Shihab A. Shamma. 2005. Multiresolution spectrotemporal analysis of complex sounds. J. Acoust. Soc. Am. 118, 2 (2005), 887-906. [OpenAIRE]

64 references, page 1 of 5
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