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Learning emotion-based acoustic features with deep belief ...
https://www.researchgate.net/publication/261465110_Learning_emotion-based_acoustic_features_with_deep_belief_networks#:~:text=Recently%2C%20Schmidt%20and%20Kim%20used%20deep%20belief%20networks,nine%20music%20emotions%20in%20the%20VA%20emotion%20space.
LEARNING FEATURES FROM MUSIC AUDIO WITH DEEP …
https://ismir2010.ismir.net/proceedings/ismir2010-58.pdf
LEARNING FEATURES FROM MUSIC AUDIO WITH DEEP BELIEF NETWORKS Philippe Hamel and Douglas Eck DIRO, Universite de Montr´ eal´ CIRMMT fhamelphi,[email protected] ABSTRACT Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that can automatically ex-tract relevant features from audio for a given task. The fea …
Learning Features from Music Audio with Deep Belief …
https://www.researchgate.net/publication/220723272_Learning_Features_from_Music_Audio_with_Deep_Belief_Networks
The feature extraction system consists of a Deep Belief Network (DBN) on Discrete Fourier Transforms (DFTs) of the audio. We then use the activations of …
Learning Features from Music Audio with Deep Belief …
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.9848
In this work, we present a system that can automatically extract relevant features from audio for a given task. The feature extraction system consists of a Deep Belief Network (DBN) on Discrete Fourier Transforms (DFTs) of the audio. We then use the activations of the trained network as inputs for a non-linear Support Vector Machine (SVM) classifier. In particular, we learned the …
Learning Features from Music Audio with Deep Belief …
https://www.semanticscholar.org/paper/Learning-Features-from-Music-Audio-with-Deep-Belief-Hamel-Eck/86e951e190586b84c530f9f03504f9ad70cc650a
This work presents a system that can automatically extract relevant features from audio for a given task by using a Deep Belief Network on Discrete Fourier Transforms of the audio to solve the task of genre recognition. Feature extraction is a crucial part of many MIR tasks.
Learning features from music audio with deep belief ...
https://citeseerx.ist.psu.edu/showciting?cid=13844357
In this work, we seek to employ regression-based deep belief networks to learn features directly from magnitude spectra. Taking into account the dynamic nature of music, we investigate combining multiple timescales of aggregated magnitude spectra as a basis for feature learning.
Advanced Music Audio Feature Learning with Deep Networks
https://scholarworks.rit.edu/cgi/viewcontent.cgi?article=10584&context=theses
deep network structures for extracting features from musical audio data represented in the frequency domain. Image-based network models are designed to be robust and accurate learners of image features. As such, this research develops image-based ImageNet deep network models to learn feature data from music audio spectrograms.
LEARNING RHYTHM AND MELODY FEATURES WITH DEEP …
https://ismir2013.ismir.net/wp-content/uploads/2013/09/193_Paper.pdf
DEEP BELIEF NETWORKS A trained deep belief network shares an identical topology to a neural network, though they offer a far-superior train- ing procedure, which begins with an unsupervised pre- training that models the hidden layers as restricted Boltz- man machines (RBMs) [4,16,17] .
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