MusicTempo_Data_GP_HEGP_SNR_CWR_Apr2022.xlsx (661.64 kB)
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Effect of Background Music Arrangement and Tempo on Foreground Speech Intelligibility: Listening experiment settings (SNRs, GP, HEGP) spreadsheets.

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posted on 12.05.2022, 17:38 by Philippa DemontePhilippa Demonte

Excel spreadsheeting containing data collected and collated from objective and subjective testing of whether or not background music arrangement (timbre and instrumentation density) and tempo have any significant effect on foreground speech intelligibility. 


The values of the objective data - speech-to-noise ratios (dB SNR), glimpse proportions (GP), and high energy glimpse proportions (HEGP) - were generated and collected in a Matlab script that incorporated Tang & Cooke's (2016) HEGP OIM (high energy glimpse proportion objective intelligibility metric) together with an interative 'for' loop.


The subjective data were collected in a standard speech-in-noise test (SINT), in which participants listened via headphones to speech played simultaneously with either background music or a control masking noise, and were tasked with identifying the final word of each spoken sentence (target word). 


The listening experiment used the RSPIN speech corpus. Background music stimuli were generated by the researcher using Apple Loops in Garage Band.


'Read Me' page provides: a brief overview of the listening experiment; citation and link for Tang and Cooke's (2016) HEGP OIM; key to explain the shorthand of the independent variables and file names, and an overview of the other spreadsheets.


'Various_GP' is an overview of equivalent speech-to-noise ratios (dB SNR) determined for three different glimpse proportion (GP) values using the speech and music masker / masking noise pairs in the Matlab script. These objective values were generated to determine which target glimpse proportion to set all the masking noise files to for the subjective listening experiment. 


'GP10_SNRs' shows two tables: one with the GP values that each masking noise file was set to and the corresponding SNRs; the other table shows this information summarised across 300 speech-noise audio file pairs.


'Results' shows the raw subjective listening experiment data collected, collated, and sorted by participant ID number, RSPIN list and RSPIN sentence number. This table has pulled in the relevant speech-to-noise ratio, glimpse proportion, and high energy glimpse proportion value from the previous page.


'Summaries' shows tables of the data collated in different ways for the purpose of generating box and whisker plots and conducting statistical analyses. Each table is a summary by participant ID (rows) and the speech-background music / masking noise combination of independent variables: total number of trials; summed correct word scores; mean correct word recognition percentages; mean speech-to-noise ratios (dB SNR); mean glimpse proportions (GP), and mean high energy glimpse proportions (HEGP)


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For further details, see PhD thesis by P. Demonte (2022), or contact: 


email (1): p.demonte@edu.salford.ac.uk

email (2): philippademonte@gmail.com 


See also the Excel spreadsheet with the listening experiment data and statistical analyses: https://doi.org/10.17866/rd.salford.19745815

'Effect of Background Music Arrangement and Tempo on Foreground Speech Intelligibiltiy: Listening experiment data'.



Funding

S3A: Future Spatial Audio for an Immersive Listener Experience at Home

Engineering and Physical Sciences Research Council

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