# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 6 Conclusions  
Automatic generation of metadata relating to timbre can facilitate searching of audio databases, and brightness is one of the most commonly used timbral descriptors; it is also the timbral attribute most affected by microphone choice. Models have been developed previously to predict the differing brightnesses of recorded sounds that result from differences between sound sources. This study developed a model intended, additionally, to account for the influence of the microphones used to make the recordings. This model is the product of the \(SC\) and \(\text{Ratio}_{2}\) (the ratio of the total magnitude of the high-frequency portion of the signal to that of the full signal), with \(\frac{1}{3}\)-octave smoothing, 500 Hz crossover frequency, and a logarithmic transformation. The model can be summarised with the flowchart shown in Figure 3.  
With a training dataset of 50 stimuli, comprising brightness ratings of five sources recorded with ten microphones, the developed model achieved a [PERSON]'s r of 0.922, [PERSON]'s rho of 0.902, and RMSE of 8.52. This performs better than all of the previously proposed metrics that relate to brightness (when calibrated on this dataset). Validating the model on a different dataset, composed of brightness ratings of six different sources recorded with ten different microphones, the proposed model achieved a [PERSON]'s r of 0.955. On this validation dataset the model, again, performed better than all previously proposed metrics.  
Conceptualization, A.P., T.B. and R.M.; methodology, A.P., T.B. and R.M.; software, A.P.; validation, A.P.; formal analysis, A.P.; investigation, A.P.; resources, A.P., T.B. and R.M.; data curation, A.P., T.B. and R.M.; writing--original draft preparation, A.P.; writing--review and editing, A.P., T.B. and R.M.; visualization, A.P. and T.B.; supervision, T.B. and R.M.; project administration, T.B. and R.M.; funding acquisition, T.B. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research was primarily funded by the UK's Engineering & Physical Sciences Research Council (EPSRC) and Cirrus Logic. Additionally, some of the work was conducted as part of the AudioCommons research project and received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 688382.  
The University of Surrey's Self-Assessment for Governance and Ethics--Human and Data Research (SAGE-HDR) process was completed and indicated that this study did not require formal review by an ethics committee.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in this study.  
Data Availability Statement:The data underlying the findings presented in this paper are available from doi:10.5281/zenodo.322747. Further project information can be found at [[https://iosr.uk/micQuality](https://iosr.uk/micQuality)]([https://iosr.uk/micQuality](https://iosr.uk/micQuality)) (accessed on 4 September 2017) and at [[https://iosr.uk/AudioCommons](https://iosr.uk/AudioCommons)]([https://iosr.uk/AudioCommons](https://iosr.uk/AudioCommons)) (accessed on 31 January 2019).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.