Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/218213
Title: A novel two-layer model for overall quality assessment of multichannel audio
Authors: Jiyue Liu;Jing Wang;Min Liu;Xiang Xie;Jingming Kuang
Year: 2017
Publisher: IEEE
Abstract: With the development of multichannel audio systems, corresponding audio quality assessment techniques, especially the objective prediction models, have received increasing attention. Existing methods, such as PEAQ (Perceptual Evaluation of Audio Quality) recommended by ITU, usually lead to poor results when assessing multichannel audio, which have little correlation with subjective scores. In this paper, a novel two-layer model based on Multiple Linear Regression (MLR) and Neural Network (NN) is proposed. Through the first layer, two indicators of multichannel audio, Audio Quality Score (AQS) and Spatial Perception Score (SPS) are derived, and through the second layer the overall score is output. The final results show that this model can not only improve the correlation with the subjective test score by 30.7% and decrease the Root Mean Square Error (RMSE) by 44.6%, but also add two new indicators: AQS and SPS, which can help reflect the multichannel audio quality more clearly.
URI: http://localhost/handle/Hannan/218213
volume: 14
issue: 9
More Information: 42,
51
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8068763.pdf918.07 kBAdobe PDF
Title: A novel two-layer model for overall quality assessment of multichannel audio
Authors: Jiyue Liu;Jing Wang;Min Liu;Xiang Xie;Jingming Kuang
Year: 2017
Publisher: IEEE
Abstract: With the development of multichannel audio systems, corresponding audio quality assessment techniques, especially the objective prediction models, have received increasing attention. Existing methods, such as PEAQ (Perceptual Evaluation of Audio Quality) recommended by ITU, usually lead to poor results when assessing multichannel audio, which have little correlation with subjective scores. In this paper, a novel two-layer model based on Multiple Linear Regression (MLR) and Neural Network (NN) is proposed. Through the first layer, two indicators of multichannel audio, Audio Quality Score (AQS) and Spatial Perception Score (SPS) are derived, and through the second layer the overall score is output. The final results show that this model can not only improve the correlation with the subjective test score by 30.7% and decrease the Root Mean Square Error (RMSE) by 44.6%, but also add two new indicators: AQS and SPS, which can help reflect the multichannel audio quality more clearly.
URI: http://localhost/handle/Hannan/218213
volume: 14
issue: 9
More Information: 42,
51
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8068763.pdf918.07 kBAdobe PDF
Title: A novel two-layer model for overall quality assessment of multichannel audio
Authors: Jiyue Liu;Jing Wang;Min Liu;Xiang Xie;Jingming Kuang
Year: 2017
Publisher: IEEE
Abstract: With the development of multichannel audio systems, corresponding audio quality assessment techniques, especially the objective prediction models, have received increasing attention. Existing methods, such as PEAQ (Perceptual Evaluation of Audio Quality) recommended by ITU, usually lead to poor results when assessing multichannel audio, which have little correlation with subjective scores. In this paper, a novel two-layer model based on Multiple Linear Regression (MLR) and Neural Network (NN) is proposed. Through the first layer, two indicators of multichannel audio, Audio Quality Score (AQS) and Spatial Perception Score (SPS) are derived, and through the second layer the overall score is output. The final results show that this model can not only improve the correlation with the subjective test score by 30.7% and decrease the Root Mean Square Error (RMSE) by 44.6%, but also add two new indicators: AQS and SPS, which can help reflect the multichannel audio quality more clearly.
URI: http://localhost/handle/Hannan/218213
volume: 14
issue: 9
More Information: 42,
51
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8068763.pdf918.07 kBAdobe PDF