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Outline

Title
Abstract
Key Takeaways
Figures
Introduction
Reviews
Test Type Reference Methodology
Secondary Analysis
Transformation of Study Data
Hypotheses and Disputed Results
Meta-Analysis Results
To What Extent Does Training Affect Results?
Effect of Test Methodology
Sensitivity Analysis
Conclusions
Implications for Practice
Implications for Experimental Design
Implications for Meta-Analysis
Future Research Directions
Appendix. Statistical Methods
Fixed Effect Meta-Analysis
Random-Effects Meta-Analysis
Meta-Analysis Test Statistics
References
All Topics
Engineering
Signal Processing

A Meta-Analysis of High Resolution Audio Perceptual Evaluation

Profile image of Josh ReissJosh Reiss
https://doi.org/10.17743/JAES.2016.0015
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16 pages

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Abstract

There is considerable debate over the benefits of recording and rendering high resolution audio, i.e., systems and formats that are capable of rendering beyond CD quality audio. We undertook a systematic review and meta-analysis to assess the ability of test subjects to perceive a difference between high resolution and standard, 16 bit, 44.1 or 48 kHz audio. All 18 published experiments for which sufficient data could be obtained were included, providing a meta-analysis involving over 400 participants in over 12,500 trials. Results showed a small but statistically significant ability of test subjects to discriminate high resolution content, and this effect increased dramatically when test subjects received extensive training. This result was verified by a sensitivity analysis exploring different choices for the chosen studies and different analysis approaches. Potential biases in studies, effect of test methodology, experimental design, and choice of stimuli were also investigated. The overall conclusion is that the perceived fidelity of an audio recording and playback chain can be affected by operating beyond conventional levels.

Key takeaways
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  1. A meta-analysis of 18 studies with over 400 participants shows a small but significant ability to discriminate high resolution audio.
  2. Training significantly enhances discrimination ability, with trained subjects showing a strong and consistent effect compared to untrained subjects.
  3. Study design factors such as stimulus duration and methodology impact discrimination results, suggesting a need for improved experimental protocols.
  4. Potential biases in existing studies may lean towards Type II errors, indicating the true discrimination ability could be underestimated.
  5. The analysis emphasizes that perceived audio fidelity increases when operating beyond conventional standards, warranting further investigation into high resolution audio.
Figures (9)
Table 1. Summary and classification of high resolution audio listening tests.
Table 1. Summary and classification of high resolution audio listening tests.
Table 2. A. List of studies included in meta-analysis. B. Risks of potential biases and other issues in the included studies (see Sec. 2.5). Low risk “-”; unclear risk “2”; high risk “[2].” The last column identifies if these risks tend strongly towards false positives (Type I errors), false negatives (Type II errors) or neither (Neutral). C. Total number of trials and correct answers for each study, with the associated binomial probability (see Sec. 3.1). may contribute towards Type II errors, i.e., an inability to demonstrate discrimination of high resolution audio. If no one was able to distinguish between formats and there were no issues in the experimental design, then all trial results would be independent, regardless of whether the tri- als were by the same participant, and regardless of how par- ticipants are categorized. But [63] also gave a breakdown of correct answers by gender, age, audio experience, and hearing ability, depicted in Table 3. Non-audiophiles, in par- ticular, have very low success rates, 30 out of 87, which has a probability of only (p(X< = 30) = 0.25%). Chi squared analysis comparing audiophiles with non-audiophiles gives ap value of 0.18%, suggesting that it is extremely unlikely that the data for these two groups are independent. Simi- larly, analysis suggests that the results for those with and
Table 2. A. List of studies included in meta-analysis. B. Risks of potential biases and other issues in the included studies (see Sec. 2.5). Low risk “-”; unclear risk “2”; high risk “[2].” The last column identifies if these risks tend strongly towards false positives (Type I errors), false negatives (Type II errors) or neither (Neutral). C. Total number of trials and correct answers for each study, with the associated binomial probability (see Sec. 3.1). may contribute towards Type II errors, i.e., an inability to demonstrate discrimination of high resolution audio. If no one was able to distinguish between formats and there were no issues in the experimental design, then all trial results would be independent, regardless of whether the tri- als were by the same participant, and regardless of how par- ticipants are categorized. But [63] also gave a breakdown of correct answers by gender, age, audio experience, and hearing ability, depicted in Table 3. Non-audiophiles, in par- ticular, have very low success rates, 30 out of 87, which has a probability of only (p(X< = 30) = 0.25%). Chi squared analysis comparing audiophiles with non-audiophiles gives ap value of 0.18%, suggesting that it is extremely unlikely that the data for these two groups are independent. Simi- larly, analysis suggests that the results for those with and
Table 3. Statistical analysis of data from [63]. Statistically significant results at « = 0.05 are given in bold.
Table 3. Statistical analysis of data from [63]. Statistically significant results at « = 0.05 are given in bold.
Fig. 2. A forest plot of studies where mean and standard deviation over all participants can be obtained, divided into subgroups based on whether participants were trained. negatives). We identified an unclear risk in each category if the risk had not been addressed or discussed and a high risk if there was strong evidence of a flaw or bias in a category. Potential biases led both to Type I and Type II errors, i.e., to falsely suggesting an ability to discriminate or not to discriminate high resolution content, though Type II errors were more common. Furthermore, biases often existed that might result in Type I errors even when the overall result demonstrated an effect (e.g., [59]). The most common way that results are presented in the studies are as the mean percentage of trials with correct discrimination of stimuli averaged over all participants. Thus this effect measure, equivalent to a mean difference [77], is used in most of the analysis that follows. The influence of these and other choices will be analyzed in Sec. 3.7.
Fig. 2. A forest plot of studies where mean and standard deviation over all participants can be obtained, divided into subgroups based on whether participants were trained. negatives). We identified an unclear risk in each category if the risk had not been addressed or discussed and a high risk if there was strong evidence of a flaw or bias in a category. Potential biases led both to Type I and Type II errors, i.e., to falsely suggesting an ability to discriminate or not to discriminate high resolution content, though Type II errors were more common. Furthermore, biases often existed that might result in Type I errors even when the overall result demonstrated an effect (e.g., [59]). The most common way that results are presented in the studies are as the mean percentage of trials with correct discrimination of stimuli averaged over all participants. Thus this effect measure, equivalent to a mean difference [77], is used in most of the analysis that follows. The influence of these and other choices will be analyzed in Sec. 3.7.
Table 4. Multiple comparisons testing. The last two rows tested the probability of a lack of significant results in multiple comparisons. The last column considers the probability of obtaining at least (or at most, for Nishiguchi 2005 and Meyer 2007) tha many significant results given the significance level and number of tests. 3 META-ANALYSIS RESULTS
Table 4. Multiple comparisons testing. The last two rows tested the probability of a lack of significant results in multiple comparisons. The last column considers the probability of obtaining at least (or at most, for Nishiguchi 2005 and Meyer 2007) tha many significant results given the significance level and number of tests. 3 META-ANALYSIS RESULTS
Fig. 3. Funnel plots of the 17 studies where a mean difference per participant was obtained, along with associated 95% confidence intervals. Much of the asymmetry in the overall funnel plot that might be attributed to publication bias is removed when funnel plots are given for subgroups of studies with and without training.
Fig. 3. Funnel plots of the 17 studies where a mean difference per participant was obtained, along with associated 95% confidence intervals. Much of the asymmetry in the overall funnel plot that might be attributed to publication bias is removed when funnel plots are given for subgroups of studies with and without training.
Table 5. Sensitivity analysis showing the percent correct (effect estimate) and confidence intervals under different approaches to the meta-analysis. Data type was considered as either continuous (CONT) for means and standard errors over all participants, or dichotomous (DIC) for number of correct responses out of all trials. Inverse Variance (IV) and Mantel-Haenszel (MH) statistical methods were considered.
Table 5. Sensitivity analysis showing the percent correct (effect estimate) and confidence intervals under different approaches to the meta-analysis. Data type was considered as either continuous (CONT) for means and standard errors over all participants, or dichotomous (DIC) for number of correct responses out of all trials. Inverse Variance (IV) and Mantel-Haenszel (MH) statistical methods were considered.
Dr. Reiss has published over 160 scientific papers, in- cluding more than 70 AES publications. His co-authored publication, “Loudness Measurement of Multitrack Audio Content Using Modifications of ITU-R BS.1770,” was re- cipient of the 134th AES Convention’s Best Peer-Reviewed Paper Award. He co-authored the textbook Audio Effects: Theory, Implementation and Application. He is co-founder of the start-up company LandR, providing intelligent tools for audio production. He is a former governor of the AES, and was Chair of the 128th, Papers Chair of the 130th, and Co-Papers Chair of the 138th AES Conventions. Josh Reiss is a Reader with Queen Mary University of London’s Centre for Digital Music, where he leads the audio engineering research team. He received his Ph.D. from Georgia Tech, specializing in analysis of nonlinear systems. His early work on sigma delta modulators led to patents and an IEEE best paper award nomination. He has investigated music retrieval systems, time scaling and pitch shifting techniques, polyphonic music transcription, oudspeaker design, automatic mixing, sound synthesis, and digital audio effects. His primary focus of research, which ties together many of the above topics, is on the use of state-of-the-art signal processing techniques for professional sound engineering.
Dr. Reiss has published over 160 scientific papers, in- cluding more than 70 AES publications. His co-authored publication, “Loudness Measurement of Multitrack Audio Content Using Modifications of ITU-R BS.1770,” was re- cipient of the 134th AES Convention’s Best Peer-Reviewed Paper Award. He co-authored the textbook Audio Effects: Theory, Implementation and Application. He is co-founder of the start-up company LandR, providing intelligent tools for audio production. He is a former governor of the AES, and was Chair of the 128th, Papers Chair of the 130th, and Co-Papers Chair of the 138th AES Conventions. Josh Reiss is a Reader with Queen Mary University of London’s Centre for Digital Music, where he leads the audio engineering research team. He received his Ph.D. from Georgia Tech, specializing in analysis of nonlinear systems. His early work on sigma delta modulators led to patents and an IEEE best paper award nomination. He has investigated music retrieval systems, time scaling and pitch shifting techniques, polyphonic music transcription, oudspeaker design, automatic mixing, sound synthesis, and digital audio effects. His primary focus of research, which ties together many of the above topics, is on the use of state-of-the-art signal processing techniques for professional sound engineering.

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