论文标题

我们还能使用Peaq吗?对感知到的音频质量的客观评估的ITU标准的性能分析

Can we still use PEAQ? A Performance Analysis of the ITU Standard for the Objective Assessment of Perceived Audio Quality

论文作者

Delgado, Pablo M., Herre, Jürgen

论文摘要

国际电信联盟(ITU)建议的音频质量(PEAQ)方法的感知评估ITU-R BS.1387已被广泛用于计算估算感知编码的音频信号的质量,而无需进行广泛的主观听力测试。但是,许多报告强调了该方案在其标准化结束后的明确局限性,尤其是涉及使用较新技术(例如带宽扩展或参数多通道编码)编码的信号。到目前为止,ITU尚无其他测量语音和音频信号质量的方法。因此,对这些限制的原因的进一步调查将对所述计划的可能更新有益。我们的实验结果表明,Peaq的干扰响度模型的性能仍然与其他最先进的客观措施(有时相比)一样好,尽管其性能变化,具体取决于降级信号内容(即语音或音乐)的类型。这一发现证明了需要改进的认知模型。此外,结果表明,基于较新的训练数据的更新模型输出值(MOVS)对PEAQ的失真指数(DI)的映射可以大大提高性能。最后,根据报告的结果和与其他系统的比较,提供了一些改善PEAQ的建议。

The Perceptual Evaluation of Audio Quality (PEAQ) method as described in the International Telecommunication Union (ITU) recommendation ITU-R BS.1387 has been widely used for computationally estimating the quality of perceptually coded audio signals without the need for extensive subjective listening tests. However, many reports have highlighted clear limitations of the scheme after the end of its standardization, particularly involving signals coded with newer technologies such as bandwidth extension or parametric multi-channel coding. Until now, no other method for measuring the quality of both speech and audio signals has been standardized by the ITU. Therefore, a further investigation of the causes for these limitations would be beneficial to a possible update of said scheme. Our experimental results indicate that the performance of PEAQ's model of disturbance loudness is still as good as (and sometimes superior to) other state-of-the-art objective measures, albeit with varying performance depending on the type of degraded signal content (i.e. speech or music). This finding evidences the need for an improved cognitive model. In addition, results indicate that an updated mapping of Model Output Values (MOVs) to PEAQ's Distortion Index (DI) based on newer training data can greatly improve performance. Finally, some suggestions for the improvement of PEAQ are provided based on the reported results and comparison to other systems.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源