# _Applied_
## 3 Methodology
### Audio Features  
The use of raw audio as input for classical machine learning models is not possible. Instead, audio features are extracted from the raw audio and serve as input. There is a wide variety in audio features and they can be broadly classified based on their semantic interpretation as perceptual and physical features. Perceptual features approximate properties that are perceived by human listeners such as pitch, loudness, rhythm and timbre. In contrast, physical features describe audio signals in terms of mathematical, statistical and physical properties. Based on the domain of representation, physical features are further divided as temporal features and spectral features. In this section, the physical features that are used during the implementation of these experimental studies are introduced. All of the audio features detailed below are provided by a specialized audio library called _librosa_[24]. * **Zero Crossing Rate (ZCR)**: The ZCR is the most common type of zero crossing based audio features [25]. It is defined as the number of time-domain zero crossings within a processing frame and indicates the frequency of signal amplitude sign change. ZCR allows for a rough estimation of dominant frequency and spectral centroid [26]. * **Root Mean Square (RMS)**: The RMS value is a measure of energy in a signal. The RMS value is defined to be the square root of the average of a squared signal [24]. * **Spectral Centroid (SC)**: SC [25] represents the \"balancing point\", or the midpoint of the spectral power distribution of a signal. It is related to the brightness of a sound. The higher the centroid, the brighter (high frequency) the sound is. A spectral centroid \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**BDLib Dataset**} & \multicolumn{2}{c}{**ESC-10 Dataset**} & \multicolumn{2}{c}{**UrbanSound Dataset**} \\ \hline
**Categories** & **Total Time (s)** & **Categories** & **Total Time (s)** & **Categories** & **Total Time (s)** \\ \hline Airplane & 100 & Dog barking & 200 & Air conditioner & 6577 \\ Alarms & 100 & Baby crying & 200 & Car horn & 4819 \\ Applause & 100 & Clock tick & 200 & Children playing & 13,454 \\ Birds & 100 & Person sneezing & 200 & Dog bark & 8399 \\ Dogs & 100 & Helicopter & 200 & Drilling & 4864 \\ Footsteps & 100 & Chainsaw & 200 & Engine idiling & 3604 \\ Motorcycles & 100 & Rooster & 200 & Gun shot & 7865 \\ Rain & 100 & Fire cracking & 200 & Jackhammer & 4328 \\ Sea waves & 100 & Sea waves & 200 & Siren & 4477 \\ Wind & 100 & Rain & 200 & Street music & 6453 \\ Rivers & 100 & & & & \\ Thunderstorm & 100 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the open source datasets used in the evaluation. provides a noise-robust estimate of how the dominant frequency of a signal changes over time. * **Spectral Bandwidth (SB)**: Usually defined as the second-order statistic of the signal spectrum, it helps to discriminate tonal sounds (with low bandwidths) from noise-like sounds (with high bandwidths) [24]. * **Spectral Roll off Point (SRP)**: The spectral roll off point is the N% percentile of the power spectral distribution, where N is usually 85% or 95% [27].The spectral roll off point is the frequency below which N% of the magnitude distribution is concentrated. It increases with the bandwidth of a signal. * **Mel Frequency Cepstral Coefficients (MFCC)**: MFCCs have been largely employed in the speech recognition field but also in the field of audio classification, due to the fact that their computation is based on a perceptual-based frequency scale in the first stage (the human auditory model which is inspired on the frequency Mel-scale). After obtaining the frame-based Fourier transform, outputs of a Mel-scale filter bank are logarithmized and finally decorrelated by means of the Discrete Cosine Transform (DCT). Only first DCT coefficients (usually from 8 to 13) are used to gather information that represents the low frequency component of the signals spectral envelope (mainly related to timbre) [24].