Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets

Zwan, Pawel; Szczuko, Piotr; Kostek, Bożena; Czyżewski, Andrzej

doi:10.1007/978-3-540-89876-4_25

Cited by 5 publications

(1 citation statement)

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“…Two intelligent methods, namely artificial neural network (ANN) and support vector machine (SVM), have been used to classify psychoacoustic metrics such as loudness, roughness, and annoyance of vehicle noise (Chen et al, 2011;Liu et al, 2015). A review of the related literature shows that the ANN is more effective for predicting the SQ in intelligent SQE systems, thanks to its good performance and adaptability to complex nonlinear problems alongside its self-learning and self-organization characteristics (Fausett, 1994;Żwan, 2008).…”

Section: Introductionmentioning

confidence: 99%

Archives of Acoustics

Pourseiedrezaei

Loghmani

Keshmiri

2019

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In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.

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Section: Introductionmentioning

confidence: 99%