Audio classification method based on non-negative tensor factorization

Lidong Yang, Xiang Xie*, Jing Wang, Jingming Kuang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

To improve the accuracy of audio classification, a classification method based on non-negative tensor factorization(NTF) was proposed. Firstly, acoustics features and perceptual features were extracted after preprocessing of audio signal. Then, a 3-order non-negative tensor was constructed, the orders being features, frames and samples, respectively. Secondly, core tensor and factor matrixes of each class of audio were obtained by using NTF. Next, test tensor was multiplied by transpose of factor matrixes of each class to obtain approximate tensor of core tensor. Finally, audio samples were classed by using Frobenius norm similarity measure. Experiments including classical music, popular music, speech and noise were provided to demonstrate the performance of audio classification. Results showed that the mean classification accuracy rate is above 85%, which proves that the proposed method can class audio effectively.

Original languageEnglish
Pages (from-to)761-764
Number of pages4
JournalTianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology
Volume48
Issue number9
DOIs
Publication statusPublished - 15 Sept 2015

Keywords

  • Audio classification
  • Factor matrix
  • Feature extraction
  • Non-negative tensor factorization

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