Файл:Musical instrument sound classification with deep convolutional neural network using feature fusion approach 1512.07370.pdf

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Musical_instrument_sound_classification_with_deep_convolutional_neural_network_using_feature_fusion_approach_1512.07370.pdf(0 × 0 пикселей, размер файла: 897 КБ, MIME-тип: application/pdf)

Taejin Park and Taejin Lee Electronics and Telecommunications Research Institute (ETRI), Republic of Korea

Abstract

A new musical instrument classification method using convolutional neural networks (CNNs) is presented in this paper. Unlike the traditional methods, we investigated a scheme for classifying musical instruments using the learned features from CNNs. To create the learned features from CNNs, we not only used a conventional spectrogram image, but also proposed multiresolution recurrence plots (MRPs) that contain the phase information of a raw input signal. Consequently, we fed the characteristic timbre of the particular instrument into a neural network, which cannot be extracted using a phase-blinded representations such as a spectrogram. By combining our proposed MRPs and spectrogram images with a multi-column network, the performance of our proposed classifier system improves over a system that uses only a spectrogram. Furthermore, the proposed classifier also outperforms the baseline result from traditional handcrafted features and classifiers.

Index Terms — Convolutional Neural Networks, Multiresolution Recurrence Plots, Musical instrument classification, Music information retrieval

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