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Applications in Music and Audio Processing (MLR-MUSI)

Deep ranking: triplet matchnet for music metric learning


Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of mu- sic styles and instrumentations.

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Authors:
Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang
Submitted On:
2 March 2017 - 2:56am
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[1] Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang, "Deep ranking: triplet matchnet for music metric learning ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1574. Accessed: Apr. 23, 2017.
@article{1574-17,
url = {http://sigport.org/1574},
author = {Rui Lu; Kailun Wu; Zhiyao Duan; Changshui Zhang },
publisher = {IEEE SigPort},
title = {Deep ranking: triplet matchnet for music metric learning },
year = {2017} }
TY - EJOUR
T1 - Deep ranking: triplet matchnet for music metric learning
AU - Rui Lu; Kailun Wu; Zhiyao Duan; Changshui Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1574
ER -
Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang. (2017). Deep ranking: triplet matchnet for music metric learning . IEEE SigPort. http://sigport.org/1574
Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang, 2017. Deep ranking: triplet matchnet for music metric learning . Available at: http://sigport.org/1574.
Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang. (2017). "Deep ranking: triplet matchnet for music metric learning ." Web.
1. Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang. Deep ranking: triplet matchnet for music metric learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1574

Song recommendation with Non-Negative Matrix factorization and graph total variation


Song recommendation with Non-Negative Matrix factorization and graph total variation

This work formulates song recommendation as a matrix completion problem that benefits from collaborative filter- ing through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using a rich combination of audio, meta-data and social features. As we demonstrate, our hybrid recom- mendation system is very versatile and incorporates several well-known methods while outperforming them. Particularly, we show on real-world data that our model overcomes w.r.t.

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Authors:
Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst
Submitted On:
20 March 2016 - 12:15am
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icassp_2016_2.pdf

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[1] Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst, "Song recommendation with Non-Negative Matrix factorization and graph total variation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/856. Accessed: Apr. 23, 2017.
@article{856-16,
url = {http://sigport.org/856},
author = {Kirell Benzi; Vassilis Kalofolias; Xavier Bresson; Pierre Vandergheynst },
publisher = {IEEE SigPort},
title = {Song recommendation with Non-Negative Matrix factorization and graph total variation},
year = {2016} }
TY - EJOUR
T1 - Song recommendation with Non-Negative Matrix factorization and graph total variation
AU - Kirell Benzi; Vassilis Kalofolias; Xavier Bresson; Pierre Vandergheynst
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/856
ER -
Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst. (2016). Song recommendation with Non-Negative Matrix factorization and graph total variation. IEEE SigPort. http://sigport.org/856
Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst, 2016. Song recommendation with Non-Negative Matrix factorization and graph total variation. Available at: http://sigport.org/856.
Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst. (2016). "Song recommendation with Non-Negative Matrix factorization and graph total variation." Web.
1. Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst. Song recommendation with Non-Negative Matrix factorization and graph total variation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/856

Emotion Classification: How Does an Automated System Compare to Naive Human Coders?


The fact that emotions play a vital role in social interactions, along with the demand for novel human-computer interaction applications, have led to the development of a number of automatic emotion classification systems. However, it is still debatable whether the performance of such systems can compare with human coders. To address this issue, in this study, we present a comprehensive comparison in a speech-based emotion classification task between 138 Amazon Mechanical Turk workers (Turkers) and a state-of-the-art automatic computer system.

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Authors:
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman
Submitted On:
17 March 2016 - 3:26pm
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EmotionICASSP16.pdf

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[1] Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman, "Emotion Classification: How Does an Automated System Compare to Naive Human Coders?", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/749. Accessed: Apr. 23, 2017.
@article{749-16,
url = {http://sigport.org/749},
author = {Kenneth Imade; Na Yang; Melissa Sturge-Apple; Zhiyao Duan; Wendi Heinzelman },
publisher = {IEEE SigPort},
title = {Emotion Classification: How Does an Automated System Compare to Naive Human Coders?},
year = {2016} }
TY - EJOUR
T1 - Emotion Classification: How Does an Automated System Compare to Naive Human Coders?
AU - Kenneth Imade; Na Yang; Melissa Sturge-Apple; Zhiyao Duan; Wendi Heinzelman
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/749
ER -
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. (2016). Emotion Classification: How Does an Automated System Compare to Naive Human Coders?. IEEE SigPort. http://sigport.org/749
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman, 2016. Emotion Classification: How Does an Automated System Compare to Naive Human Coders?. Available at: http://sigport.org/749.
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. (2016). "Emotion Classification: How Does an Automated System Compare to Naive Human Coders?." Web.
1. Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. Emotion Classification: How Does an Automated System Compare to Naive Human Coders? [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/749

Emotion Classification: How Does an Automated System Compare to Naive Human Coders?


The fact that emotions play a vital role in social interactions, along with the demand for novel human-computer interaction applications, have led to the development of a number of automatic emotion classification systems. However, it is still debatable whether the performance of such systems can compare with human coders. To address this issue, in this study, we present a comprehensive comparison in a speech-based emotion classification task between 138 Amazon Mechanical Turk workers (Turkers) and a state-of-the-art automatic computer system.

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Authors:
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman
Submitted On:
17 March 2016 - 3:26pm
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EmotionICASSP16.pdf

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[1] Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman, "Emotion Classification: How Does an Automated System Compare to Naive Human Coders?", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/748. Accessed: Apr. 23, 2017.
@article{748-16,
url = {http://sigport.org/748},
author = {Kenneth Imade; Na Yang; Melissa Sturge-Apple; Zhiyao Duan; Wendi Heinzelman },
publisher = {IEEE SigPort},
title = {Emotion Classification: How Does an Automated System Compare to Naive Human Coders?},
year = {2016} }
TY - EJOUR
T1 - Emotion Classification: How Does an Automated System Compare to Naive Human Coders?
AU - Kenneth Imade; Na Yang; Melissa Sturge-Apple; Zhiyao Duan; Wendi Heinzelman
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/748
ER -
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. (2016). Emotion Classification: How Does an Automated System Compare to Naive Human Coders?. IEEE SigPort. http://sigport.org/748
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman, 2016. Emotion Classification: How Does an Automated System Compare to Naive Human Coders?. Available at: http://sigport.org/748.
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. (2016). "Emotion Classification: How Does an Automated System Compare to Naive Human Coders?." Web.
1. Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. Emotion Classification: How Does an Automated System Compare to Naive Human Coders? [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/748

Emotion Classification: How Does an Automated System Compare to Naive Human Coders?


The fact that emotions play a vital role in social interactions, along with the demand for novel human-computer interaction applications, have led to the development of a number of automatic emotion classification systems. However, it is still debatable whether the performance of such systems can compare with human coders. To address this issue, in this study, we present a comprehensive comparison in a speech-based emotion classification task between 138 Amazon Mechanical Turk workers (Turkers) and a state-of-the-art automatic computer system.

Paper Details

Authors:
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman
Submitted On:
17 March 2016 - 3:26pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

EmotionICASSP16.pptx

(127 downloads)

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[1] Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman, "Emotion Classification: How Does an Automated System Compare to Naive Human Coders?", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/747. Accessed: Apr. 23, 2017.
@article{747-16,
url = {http://sigport.org/747},
author = {Kenneth Imade; Na Yang; Melissa Sturge-Apple; Zhiyao Duan; Wendi Heinzelman },
publisher = {IEEE SigPort},
title = {Emotion Classification: How Does an Automated System Compare to Naive Human Coders?},
year = {2016} }
TY - EJOUR
T1 - Emotion Classification: How Does an Automated System Compare to Naive Human Coders?
AU - Kenneth Imade; Na Yang; Melissa Sturge-Apple; Zhiyao Duan; Wendi Heinzelman
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/747
ER -
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. (2016). Emotion Classification: How Does an Automated System Compare to Naive Human Coders?. IEEE SigPort. http://sigport.org/747
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman, 2016. Emotion Classification: How Does an Automated System Compare to Naive Human Coders?. Available at: http://sigport.org/747.
Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. (2016). "Emotion Classification: How Does an Automated System Compare to Naive Human Coders?." Web.
1. Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi Heinzelman. Emotion Classification: How Does an Automated System Compare to Naive Human Coders? [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/747

Feature Adapted Convolutional Neural Networks for Downbeat Tracking


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Authors:
Durand, S. and Bello, J. P and Bertrand, D. and Richard, G.
Submitted On:
14 March 2016 - 2:03pm
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[1] Durand, S. and Bello, J. P and Bertrand, D. and Richard, G., "Feature Adapted Convolutional Neural Networks for Downbeat Tracking", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/678. Accessed: Apr. 23, 2017.
@article{678-16,
url = {http://sigport.org/678},
author = {Durand; S. and Bello; J. P and Bertrand; D. and Richard; G. },
publisher = {IEEE SigPort},
title = {Feature Adapted Convolutional Neural Networks for Downbeat Tracking},
year = {2016} }
TY - EJOUR
T1 - Feature Adapted Convolutional Neural Networks for Downbeat Tracking
AU - Durand; S. and Bello; J. P and Bertrand; D. and Richard; G.
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/678
ER -
Durand, S. and Bello, J. P and Bertrand, D. and Richard, G.. (2016). Feature Adapted Convolutional Neural Networks for Downbeat Tracking. IEEE SigPort. http://sigport.org/678
Durand, S. and Bello, J. P and Bertrand, D. and Richard, G., 2016. Feature Adapted Convolutional Neural Networks for Downbeat Tracking. Available at: http://sigport.org/678.
Durand, S. and Bello, J. P and Bertrand, D. and Richard, G.. (2016). "Feature Adapted Convolutional Neural Networks for Downbeat Tracking." Web.
1. Durand, S. and Bello, J. P and Bertrand, D. and Richard, G.. Feature Adapted Convolutional Neural Networks for Downbeat Tracking [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/678