Sorry, you need to enable JavaScript to visit this website.

Audio Analysis and Synthesis

Robust Fundamental Frequency Estimation in Coloured Noise


Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaussian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account.

Paper Details

Authors:
Andreas Jakobsson, Mads Græsbøll Christensen
Submitted On:
8 May 2020 - 12:48pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

pitch in colored noise

(31)

Subscribe

[1] Andreas Jakobsson, Mads Græsbøll Christensen, "Robust Fundamental Frequency Estimation in Coloured Noise", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5120. Accessed: Jul. 02, 2020.
@article{5120-20,
url = {http://sigport.org/5120},
author = {Andreas Jakobsson; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {Robust Fundamental Frequency Estimation in Coloured Noise},
year = {2020} }
TY - EJOUR
T1 - Robust Fundamental Frequency Estimation in Coloured Noise
AU - Andreas Jakobsson; Mads Græsbøll Christensen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5120
ER -
Andreas Jakobsson, Mads Græsbøll Christensen. (2020). Robust Fundamental Frequency Estimation in Coloured Noise. IEEE SigPort. http://sigport.org/5120
Andreas Jakobsson, Mads Græsbøll Christensen, 2020. Robust Fundamental Frequency Estimation in Coloured Noise. Available at: http://sigport.org/5120.
Andreas Jakobsson, Mads Græsbøll Christensen. (2020). "Robust Fundamental Frequency Estimation in Coloured Noise." Web.
1. Andreas Jakobsson, Mads Græsbøll Christensen. Robust Fundamental Frequency Estimation in Coloured Noise [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5120

VaPar Synth - A Variational Parametric Model for Audio Synthesis


With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to model audio signals directly in the time or frequency domain. In the interest of more flexible control over the generated sound, it could be more useful to work with a parametric representation of the signal which corresponds more directly to the musical attributes such as pitch, dynamics and timbre.

Paper Details

Authors:
Krishna Subramani, Preeti Rao, Alexandre D'Hooge
Submitted On:
18 April 2020 - 2:10am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Presentation Slides

(31)

Subscribe

[1] Krishna Subramani, Preeti Rao, Alexandre D'Hooge, "VaPar Synth - A Variational Parametric Model for Audio Synthesis", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5104. Accessed: Jul. 02, 2020.
@article{5104-20,
url = {http://sigport.org/5104},
author = {Krishna Subramani; Preeti Rao; Alexandre D'Hooge },
publisher = {IEEE SigPort},
title = {VaPar Synth - A Variational Parametric Model for Audio Synthesis},
year = {2020} }
TY - EJOUR
T1 - VaPar Synth - A Variational Parametric Model for Audio Synthesis
AU - Krishna Subramani; Preeti Rao; Alexandre D'Hooge
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5104
ER -
Krishna Subramani, Preeti Rao, Alexandre D'Hooge. (2020). VaPar Synth - A Variational Parametric Model for Audio Synthesis. IEEE SigPort. http://sigport.org/5104
Krishna Subramani, Preeti Rao, Alexandre D'Hooge, 2020. VaPar Synth - A Variational Parametric Model for Audio Synthesis. Available at: http://sigport.org/5104.
Krishna Subramani, Preeti Rao, Alexandre D'Hooge. (2020). "VaPar Synth - A Variational Parametric Model for Audio Synthesis." Web.
1. Krishna Subramani, Preeti Rao, Alexandre D'Hooge. VaPar Synth - A Variational Parametric Model for Audio Synthesis [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5104

AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications


For their analysis with conventional signal processing tools, non-stationary signals are assumed to be stationary (or at least wide-sense stationary) in short intervals. While this approach allows them to be studied, it disregards the temporal evolution of their statistics. As such, to analyze this type of signals, it is desirable to use a representation that registers and characterizes the temporal changes in the frequency content of the signals, as these changes may occur in single or multiple periodic ways.

Paper Details

Authors:
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk
Submitted On:
7 November 2019 - 7:21pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster

(82)

Subscribe

[1] Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4927. Accessed: Jul. 02, 2020.
@article{4927-19,
url = {http://sigport.org/4927},
author = {Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk },
publisher = {IEEE SigPort},
title = {AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications},
year = {2019} }
TY - EJOUR
T1 - AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications
AU - Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4927
ER -
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. IEEE SigPort. http://sigport.org/4927
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, 2019. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. Available at: http://sigport.org/4927.
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications." Web.
1. Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4927

DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS


Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional postfilters that are widely used in SPSS systems for increasing the quality of synthesized speech. However, existing DNN-based postfilters are trained with speaker-dependent databases. Given that SPSS systems can rapidly adapt to new speakers from generic models, there is a need for DNN-based postfilters that can adapt to new speakers with minimal adaptation data.

Paper Details

Authors:
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu
Submitted On:
10 May 2019 - 7:36am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_2019_v1.pptx

(82)

Subscribe

[1] Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu, "DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4306. Accessed: Jul. 02, 2020.
@article{4306-19,
url = {http://sigport.org/4306},
author = {Miraç Göksu Öztürk; Okan Ulusoy; Cenk Demiroglu },
publisher = {IEEE SigPort},
title = {DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS},
year = {2019} }
TY - EJOUR
T1 - DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS
AU - Miraç Göksu Öztürk; Okan Ulusoy; Cenk Demiroglu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4306
ER -
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu. (2019). DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS. IEEE SigPort. http://sigport.org/4306
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu, 2019. DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS. Available at: http://sigport.org/4306.
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu. (2019). "DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS." Web.
1. Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu. DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4306

F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT


Pitch plays a significant role in understanding a tone based language like Mandarin. In this paper, we present a new method that estimates F0 contour for electrolaryngeal (EL) speech enhancement in Mandarin. Our system explores the usage of phonetic feature to improve the quality of EL speech. First, we train an acoustic model for EL speech and generate the phoneme posterior probabilities feature sequence for each input EL speech utterance. Then we employ the phonetic feature for F0 contour generation rather than the acoustic feature.

Paper Details

Authors:
Zexin Cai, Zhicheng Xu, Ming Li
Submitted On:
7 May 2019 - 11:25pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP.2019.8683435-poster

(72)

Subscribe

[1] Zexin Cai, Zhicheng Xu, Ming Li, "F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4000. Accessed: Jul. 02, 2020.
@article{4000-19,
url = {http://sigport.org/4000},
author = {Zexin Cai; Zhicheng Xu; Ming Li },
publisher = {IEEE SigPort},
title = {F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT},
year = {2019} }
TY - EJOUR
T1 - F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT
AU - Zexin Cai; Zhicheng Xu; Ming Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4000
ER -
Zexin Cai, Zhicheng Xu, Ming Li. (2019). F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT. IEEE SigPort. http://sigport.org/4000
Zexin Cai, Zhicheng Xu, Ming Li, 2019. F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT. Available at: http://sigport.org/4000.
Zexin Cai, Zhicheng Xu, Ming Li. (2019). "F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT." Web.
1. Zexin Cai, Zhicheng Xu, Ming Li. F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4000

Tutorial T-9: Model-based Speech and Audio Processing

Paper Details

Authors:
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen
Submitted On:
16 April 2018 - 5:52pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Slides

(628)

References

(315)

Subscribe

[1] Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen, "Tutorial T-9: Model-based Speech and Audio Processing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2917. Accessed: Jul. 02, 2020.
@article{2917-18,
url = {http://sigport.org/2917},
author = {Mads Græsbøll Christensen; Jesper Kjær Nielsen; and Jesper Rindom Jensen },
publisher = {IEEE SigPort},
title = {Tutorial T-9: Model-based Speech and Audio Processing},
year = {2018} }
TY - EJOUR
T1 - Tutorial T-9: Model-based Speech and Audio Processing
AU - Mads Græsbøll Christensen; Jesper Kjær Nielsen; and Jesper Rindom Jensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2917
ER -
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen. (2018). Tutorial T-9: Model-based Speech and Audio Processing. IEEE SigPort. http://sigport.org/2917
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen, 2018. Tutorial T-9: Model-based Speech and Audio Processing. Available at: http://sigport.org/2917.
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen. (2018). "Tutorial T-9: Model-based Speech and Audio Processing." Web.
1. Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen. Tutorial T-9: Model-based Speech and Audio Processing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2917

Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations


Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations.

Paper Details

Authors:
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello
Submitted On:
14 April 2018 - 5:17pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

cartwright_icassp_2018_poster.pdf

(269)

Subscribe

[1] Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello, "Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2853. Accessed: Jul. 02, 2020.
@article{2853-18,
url = {http://sigport.org/2853},
author = {Mark Cartwright; Justin Salamon; Ayanna Seals; Oded Nov; Juan Pablo Bello },
publisher = {IEEE SigPort},
title = {Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations},
year = {2018} }
TY - EJOUR
T1 - Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations
AU - Mark Cartwright; Justin Salamon; Ayanna Seals; Oded Nov; Juan Pablo Bello
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2853
ER -
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello. (2018). Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations. IEEE SigPort. http://sigport.org/2853
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello, 2018. Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations. Available at: http://sigport.org/2853.
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello. (2018). "Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations." Web.
1. Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello. Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2853

SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS


This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information.

Paper Details

Authors:
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling
Submitted On:
13 April 2018 - 3:29am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018_poster_aiyang.pdf

(285)

Subscribe

[1] Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2633. Accessed: Jul. 02, 2020.
@article{2633-18,
url = {http://sigport.org/2633},
author = {Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling },
publisher = {IEEE SigPort},
title = {SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS
AU - Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2633
ER -
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. IEEE SigPort. http://sigport.org/2633
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, 2018. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. Available at: http://sigport.org/2633.
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS." Web.
1. Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2633

SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS


This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information.

Paper Details

Authors:
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling
Submitted On:
13 April 2018 - 3:29am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster

(365)

Subscribe

[1] Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2632. Accessed: Jul. 02, 2020.
@article{2632-18,
url = {http://sigport.org/2632},
author = {Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling },
publisher = {IEEE SigPort},
title = {SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS
AU - Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2632
ER -
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. IEEE SigPort. http://sigport.org/2632
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, 2018. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. Available at: http://sigport.org/2632.
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS." Web.
1. Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2632

REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS


Being able to predict whether a song can be a hit has important applications in the music industry. Although it is true that the popularity of a song can be greatly affected by external factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own.

Paper Details

Authors:
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen
Submitted On:
3 March 2017 - 12:59am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp2017.pdf

(546)

Subscribe

[1] Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen, "REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1600. Accessed: Jul. 02, 2020.
@article{1600-17,
url = {http://sigport.org/1600},
author = {Li-Chia Yang; Szu-Yu Chou; Jen-Yu Liu; Yi-Hsuan Yang; Yi-An Chen },
publisher = {IEEE SigPort},
title = {REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS},
year = {2017} }
TY - EJOUR
T1 - REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS
AU - Li-Chia Yang; Szu-Yu Chou; Jen-Yu Liu; Yi-Hsuan Yang; Yi-An Chen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1600
ER -
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen. (2017). REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/1600
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen, 2017. REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/1600.
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen. (2017). "REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen. REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1600

Pages