TY - JOUR
T1 - Audio classification method based on non-negative tensor factorization
AU - Yang, Lidong
AU - Xie, Xiang
AU - Wang, Jing
AU - Kuang, Jingming
N1 - Publisher Copyright:
©, 2015, Tianjin University. All right reserved.
PY - 2015/9/15
Y1 - 2015/9/15
N2 - 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.
AB - 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.
KW - Audio classification
KW - Factor matrix
KW - Feature extraction
KW - Non-negative tensor factorization
UR - http://www.scopus.com/inward/record.url?scp=84944208760&partnerID=8YFLogxK
U2 - 10.11784/tdxbz201507041
DO - 10.11784/tdxbz201507041
M3 - Article
AN - SCOPUS:84944208760
SN - 0493-2137
VL - 48
SP - 761
EP - 764
JO - Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology
JF - Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology
IS - 9
ER -