# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 3 Materials and Methods
### Calculating Required Voice Characteristics  
To conduct the clinical research, we built a universal-platform-based version of the \"VoiceScreen\" software for use with both iOS and Android operating systems, using our Pareto optimized technique. The AVQI and its characteristics are calculated on the server; hence, computationally expensive sound processing is not reliant on the computing capabilities of the user device and may run on any Android or iOS device with a manufacturer-supported version of each respective operating system. The provided smartphone (either iOS or Android) records sound waves obtained while pronouncing given sentences aloud in the first stage. Sound waves are preprocessed in real time. The goal is to remove pauses from the sound waves and to guarantee that only the minimum quantity of sound is available for further processing. Then, that preprocessed sound wave is sent to the server for further analysis. The server runs a Linux operating system and operates our proprietary software to calculate the required characteristics necessary to evaluate the voice. Finally, the AVQI index and related data are sent back to the phone and displayed to the user. Figure 4 shows the structure of the system, while Figure 5 illustrates the operation sequence. The server-side system performs numerous operations. First, the cepstral peak prominence (CPPS) is calculated (see Figure 6). Our approach outlines a series of algorithms used to assess impaired voice quality using acoustic measures. The first algorithm resamples and applies pre-emphasis to the sound, then calculates the power cepstrogram. The second algorithm calculates the harmonicity using the cross-correlation technique, while the third algorithm calculates the shimmer, which measures cycle-to-cycle variations in vocal amplitude. The fourth algorithm calculates the long-term average spectrum (LTAS) of the sound waveform. Finally, the AVQI values are calculated, using multiple acoustic measures of voice quality through Pareto optimization. This technique identifies the optimal trade-off between multiple conflicting objectives to find the best solution to improve overall vocal quality while minimizing breathiness, roughness, and strain. In Algorithm 1, we first resample the sound to twice the value of the maximum frequency and apply pre-emphasis to the resampled sound. Then, for each analysis window, we apply a Gaussian window, calculate the spectrum, transform it into a PowerCepstrum and store the values in the corresponding vertical slice of the PowerCepstrogram matrix. The algorithm returns the resulting PowerCepstrogram. The PowerCepstrogram is a representation of the power spectrum of a signal in the cepstral domain. The algorithm takes as input a sound signal, a pitch floor, a time step, a maximum frequency, and a pre-emphasis coefficient. The output of the algorithm is the PowerCepstrogram. Figure 3: Spectral analysis results of a normal voice (class 0) and voice with severe dysphonia. Figure 4: Structure of the system and flow of the operations. Figure 5: Sequence diagram of operations performed in our approach.