## 1 Introduction
Bars represent the mean value \(\pm\) standard deviation (95% confidence interval). (Adapted and with the courtesy of (25)). In the literature, there are basically three methods for determining the HNR. These methods are based on the time, spectral, and cepstral domains. All methods are applied to a short-time signal window. The algorithm proposed here is based on the time domain, and the results are compared with another method based on the time domain, as is the case of [PERSON]'s algorithm [27]. [PERSON]'s algorithm uses the second peak of the normalized Autocorrelation to determine the HNR. The differences are detailed in Section 2.4. Algorithms based on spectral components separate the signal spectrum into harmonic and noise regions, considering peaks as the center of harmonic regions and valleys as noise regions. [PERSON] and [PERSON], 1995 [28], is based on the iterative reconstruction of the signal. Initially, using Linear Predictive (LP) analysis, speech is separated into approximate excitation and filter components. Next, noise frequency regions and deterministic excitation components are identified using the cepstrum. Two excitation components are reconstructed using an iterative algorithm. Finally, the deterministic and stochastic components of the excitation are obtained by combining the reconstructed data frames using an overlay and addition procedure. These components are passed through the time-varying all-pole filter to get the speech signal components. In this method, it is considered that the spectral peaks correspond to the information of the harmonic component, and the valleys correspond to the noise. Additionally, in 2009, [PERSON] [29] proposed a method where initially the signal is segmented into frames, and then a sine wave is applied. The harmonic component is estimated from the Odd Discrete Fourier Transform (ODFT) of the signal by extracting each harmonic's frequency, magnitude and phase. In the ODFT domain, the parameters of the harmonic structure are easily measured and are not significantly affected by noise. These parameters are used to synthesize the harmonic structure in the ODFT domain. The harmonic component is subtracted from the complete signal, producing the noise component estimate. Based on a comparison with other methods, the author mentioned that this algorithm is more suitable for high\(f_{\circ}\) signals (female voices). [PERSON] in 1993 [30], proposed an algorithm based on spectral components. However, this algorithm seems to have some difficulties in determining the peaks and valleys, which sometimes arise when there is a peak with a low amplitude followed by one with a high amplitude. Algorithms based on cepstral analysis consider that the harmonic components are represented by the rahmonic peaks (cepstrum peaks), and the noise components are concentrated in the low quefrencies. The segmentation of zones with harmonic or noise components is based on short-pass or comb lifters, adequately dimensioned. These lifters (filters in the cepstral domain) estimate the noise's spectral baseline, which allows the determination of the HNR value [31]. The NHR parameter quantifies the relationship between the aperiodic component (noise) and the periodic component (harmonic part) [32]. This parameter is rarely used because it is directly related to the Autocorrelation, as described below. NHR has very low values for normophonic voices and can be slightly higher for some pathological voices. The development of an algorithm that allows for determining these parameters is presented. As a reference, version 6.0.33 Praat software's output values for the same acoustic signals are used for comparison. This software is generally used by the scientific community and speech therapeutic professionals. It should be mentioned that the Praat algorithm has a higher complexity level than the one proposed in this manuscript because it determines several candidates for the peaks of the Autocorrelation [27].