In this paper, we propose an architecture based on a stacked auto-encoder (SAE) for the classification of music genre. Each level in the stacked architecture works by stacking some hidden representations resulting from the previous level and related to different frames of the input signal.
Sep 19, 2019
We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which ...
In this work, five interesting and novel approaches are proposed for music genre classification such as the proposed Weighted Visibility Graph based Elastic ...
Music Genre Classification Using Stacked Auto-Encoders | Semantic ...
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The proposed architecture is compared to the support vector machine (SVM), multi-layer perceptron (MLP) and logistic regression (LR) for the classification ...
Mar 9, 2020 · [Tutorial] I implemented a neural network that performs automatic music genre classification. ... I'm using the Marsayas genre dataset ...
Missing: Stacked Encoders.
Dec 1, 2022 · This paper studies the impact of different tuning parameters of the stacked auto-encoder under different experiments. Classification accuracy is ...
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In this chapter, we propose an architecture based on a stacked auto-encoder (SAE) for the classification of music genre. Each level in the stacked ...
Jan 17, 2024 · The classification methods used in this study are: the weighted visibility graph based elastic net sparse classifier (WVG-ELNSC), the sequential ...
Music Genre Classification Using Stacked Auto-Encoders. Chapter. Jan 2020 ... stacked auto-encoder (SAE) for the classification of music genre. Each ...
To make a deep network we can stack these auto en- coder on top of each others. This means that the code(learned hidden representation) of the kth − 1 layer ...