Sparse Coding Methods for Music Induced Emotion Recognition

Jakubik, Jan; Kwaśnicka, Halina

doi:10.15439/2016f309

Cited by 4 publications

(1 citation statement)

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“…Codebook methods have been shown to learn useful features even in shallow architectures [13][14][15]. The use of simple autoencoder neural network to learn features on a spectrogram for predicting community consensus task with GEMS categories gives comparable results as traditional machine learning with the use of a manually well-chosen set of features [16]. Deep learning improves these results further, resulting in state-of-the-art performance.…”

Section: Introductionmentioning

confidence: 99%

Similarity‐Based Summarization of Music Files for Support Vector Machines

Jakubik

Kwaśnicka

2018

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Automatic retrieval of music information is an active area of research in which problems such as automatically assigning genres or descriptors of emotional content to music emerge. Recent advancements in the area rely on the use of deep learning, which allows researchers to operate on a low-level description of the music. Deep neural network architectures can learn to build feature representations that summarize music files from data itself, rather than expert knowledge. In this paper, a novel approach to applying feature learning in combination with support vector machines to musical data is presented. A spectrogram of the music file, which is too complex to be processed by SVM, is first reduced to a compact representation by a recurrent neural network. An adjustment to loss function of the network is proposed so that the network learns to build a representation space that replicates a certain notion of similarity between annotations, rather than to explicitly make predictions. We evaluate the approach on five datasets, focusing on emotion recognition and complementing it with genre classification. In experiments, the proposed loss function adjustment is shown to improve results in classification and regression tasks, but only when the learned similarity notion corresponds to a kernel function employed within the SVM. These results suggest that adjusting deep learning methods to build data representations that target a specific classifier or regressor can open up new perspectives for the use of standard machine learning methods in music domain.

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Section: Introductionmentioning

confidence: 99%