Abstract
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.
Highlights
In our digital world, there are huge resources of data, images, video, and music
(ii) support vector machine (SVM) with baseline feature learning approach (FL): features are taken from the penultimate layer of the Gated recurrent unit (GRU) neural network trained to classify or predict, an SVM is trained on them
(iii) SVM with neural network approach (NN) learning Radial basis function (RBF) similarity (RBF-SL): feature extracting GRU neural network is learned with similarity-based loss using RBF kernel (γ = 0 5) for similarity, SVM is trained on output features of the network
Summary
In our digital world, there are huge resources of data, images, video, and music. Advanced methods of automatic processing of music resources remain in the sphere of interest of many researchers. The goal is to facilitate music information retrieval (MIR) in a personalized way for the needs of an individual user. Despite the involvement of researchers and use of state-of-the-art methods, such as deep learning, there is a lack of advanced search engines, especially able to take into account users’ personal preferences. First is the need for automatic organizing of music collections, and the second is how to automatically recommend new songs to a particular user, taking into account the user’s listening habit [1]. To recommend a song according to user’s expectations, it is beneficial to automatically recognize the emotions that a song induces to the user and the genre to which a song belongs
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