# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 3 Experimental Validation
### Performance Evaluation  
Our presented novel methodology differs from conventional research in the fact that users can create their personalized soundscaping algorithms by performing automated probabilistic inference on a modular generative model, with interchangeable submodels trained using on-the-spot recorded sound fragments. Users can adjust the source-specific gains \(w\) for balancing the amount of noise reduction and the inevitable introduction of speech distortion. Throughout the experiments the parameter \(w\) is post-hoc optimized subject to \(w^{k}\in[0,1]\) and \(\sum_{k}w^{k}=1\) to yield the most optimal performance metric, as in a real setting we assume that the user is capable of setting these parameters to their most optimal value. For comparison we will also evaluate the performance of the noise corrupted situation before and after processing with a Wiener filter [56].  
A Wiener filter assumes full knowledge about the underlying signals and was implemented as follows. The mixture signal and the underlying signals are all processed by separate frequency-warped filters Every segment of data, consisting of 32 samples, is fed into the frequency warped filter. The frequency coefficients are extracted using the STFT and based on those the signal powers are calculated for each frequency bin as their squared magnitudes. The Wiener filter gain for each of the frequency bins is calculated individually as \(G^{f}=P_{s}^{f}/(P_{s}^{f}+P_{\backslash s}^{f})\), where \(G^{f}\) is the Wiener filter gain for frequency bin \(f\) and where \(P_{s}^{f}\) and \(P_{\backslash s}^{f}\) represent the corresponding calculated powers of the speech signal and noise signal, respectively. This gain is applied to the FIR compression filter to determine the processed output signal.  
Finally, a quantitative measure for assessing the performance is not straightforward, because the performance depends on the perception of a specific individual human listener, and there are no personalized metrics for this application. In order to evaluate our approach we evaluate several metrics, of which some have been developed to approximate human perception. In this paper we evaluate the model performance using the output SNR, the perceptual evaluation of speech quality (PESQ) metric [57] and the short-time objective intelligibility measure (STOI) metric [58]. The output SNR represents the ratio of signal power with respect to noise power. It gives a quantitative impression of the remaining noise of the denoised signal by comparing it with the true signal. However, the output SNR does not measure the perceptual performance of the noise reduction algorithms. In contrast, the PESQ metric [57], introduced in 2001, is a more advanced metric that has been introduced to better model the perceptual performance. It was intended for a wider variety of network conditions and has been show to yield a significantly higher correlation with subjective opinions with respect to other perceptual evaluation metrics. The STOI metric [58], introduced in 2011, provides a measure for speech intelligibility that only uses short-time temporal speech segments, based on the correlation between the temporal envelopes of the extracted and true speech signal. It is important to note here that the PESQ and STOI metric represent by definition a group average of the experienced perceptual quality. The PESQ scores range from 1.0 (worst) to 4.5 (best) and the STOI scores range from 0.0 (worst) to 1.0 (best).