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Towards the automatic assessment of spatial quality in the reproduced sound environment

机译:在再现的声音环境中自动评估空间质量

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摘要

The research in this thesis describes the creation and development of a method for the prediction of perceived spatial quality. The QESTRAL (Quality Evaluation of Spatial Transmission and Reproduction using an Artificial Listener) model is an objective evaluation model capable of accurately predicting changes to perceived spatial quality. It uses probe signals and a set of objective metrics to measure changes to low-level spatial attributes. A polynomial weighting function derived from regression analysis is used to predict data from listening tests, which employed spatial audio processes (SAPs) proven to stress those low-level attributes. A listening test method was developed for collecting listener judgements of impairments to spatial quality. This involved the creation of a novel test interface to reduce the biases inherent in other similar audio quality assessment tests. Pilot studies were undertaken which established the suitability of the method. Two large scale listening tests were conducted using 31 Tonmeister students from the Institute of Sound Recording (IoSR), University of Surrey. These tests evaluated 48 different SAPs, typically encountered in consumer sound reproduction equipment, when applied to 6 types of programme material. The tests were conducted at two listening positions to determine how perceived spatial quality was changed. Analysis of the data collected from these listening tests showed that the SAPs created a diverse range of judgements that spanned the range of the spatial quality test scale and that listening position, programme material type and listener each had a statistically significant influence upon perceived spatial quality. These factors were incorporated into a database of 308 responses used to calibrate the model. The model was calibrated using partial least-squares regression using target specifications similar to those of audio quality models created by other researchers. This resulted in five objective metrics being selected for use in the model. A method of post correction using an exponential equation was used to reduce non-linearity in the predicted results, thought to be caused by the inability of some metrics to scrutinise the highest quality SAPs. The resulting model had a correlation (r) of 0.89 and an error (RMSE) of 11.06% and performs similarly to models developed by other researchers. Statistical analysis also indicated that the model would generalise to a larger population of listeners.
机译:本文的研究描述了一种感知空间质量预测方法的创建和发展。 QESTRAL(使用人工监听器进行空间传输和复制的质量评估)模型是一种客观评估模型,能够准确预测感知到的空间质量的变化。它使用探测信号和一组客观指标来度量低级空间属性的变化。从回归分析得出的多项式加权函数用于预测听力测试的数据,听力测试采用了被证明可以强调那些低级属性的空间音频过程(SAP)。开发了一种听力测试方法,用于收集听众对空间质量损害的判断。这涉及创建新颖的测试界面,以减少其他类似音频质量评估测试中固有的偏差。进行了初步研究,确定了该方法的适用性。来自萨里大学的录音研究所(IoSR)的31名Tonmeister学生进行了两次大规模的听力测试。当将这些测试方法应用于6种类型的程序材料时,它们评估了48种不同的SAP,它们通常在消费类声音再现设备中遇到。测试在两个聆听位置进行,以确定感知的空间质量如何变化。对从这些听力测试中收集到的数据的分析表明,SAP创建了跨越空间质量测试规模范围的各种判断,并且听力位置,节目材料类型和听众都对感知的空间质量产生了统计学上的显着影响。这些因素被合并到用于校准模型的308个响应的数据库中。使用与其他研究人员创建的音频质量模型相似的目标规范,使用偏最小二乘回归对模型进行了校准。这导致选择了五个客观指标以用于模型。一种使用指数方程的后期校正方法被用来减少预测结果中的非线性,这被认为是由于某些度量标准无法审查最高质量的SAP所致。所得模型的相关系数(r)为0.89,误差(RMSE)为11.06%,其性能与其他研究人员开发的模型相似。统计分析还表明,该模型将推广到更多的听众群体。

著录项

  • 作者

    Conetta R;

  • 作者单位
  • 年度 2011
  • 总页数
  • 原文格式 PDF
  • 正文语种 English
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