## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.2 STFT-Based Frequency Features  
This kind of audio features are generally derived from the signal spectrogram obtained from STFT computation. While some of the features belonging to this category are computed from the analysis of the spectrogram envelope (e.g., subband energy ratio, spectral flux, spectral slope, spectral peaks or MPEG-7 spectral envelope, normalized spectral envelope, and stereo panning spectrum feature), others are obtained from the STFT phase (like group delay functions and/or modified group delay functions). The following list summarizes the most widely employed STFT-based features. * **Subband energy ratio:** it is usually defined as a measure of the normalized signal energy along a predefined set of frequency subbands. In a broad sense, it coarsely describes the signal energy distribution of the spectrum ([PERSON] _et al._[17]). There are different approximations as regards the number and characteristics of analyzed subbands (e.g., Mel scale, ad-hoc subbands, _etc._). It has been used for audio segmentation and music analysis applications (see [PERSON] _et al._[60], or [PERSON] _et al._[74]) and environmental sound recognition ([PERSON] _et al._[18]). * **Spectral flux** or SF for short, this feature is defined as the 2-norm of the frame-to-frame spectral amplitude difference vector (see [PERSON] and [PERSON] [61]), and it describes sudden changes in the frequency energy distribution of sounds, which can be applied for detection of musical note onsets or, more generally speaking, detection of significant changes in the spectral distribution. It measures how quickly the power spectrum changes and it can be used to determine the timbre of an audio signal. This feature has been used for speech/music discrimination (like in [PERSON] _et al._[60], or in [PERSON] _et al._[68,69]), musical instrument classification ([PERSON] _et al._[10]), music genre classification ([PERSON] _et al._[40], [PERSON] _et al._[12], [PERSON] and [PERSON] [28], [PERSON] _et al._[9]) and environmental sound recognition (see [PERSON] _et al._[18]). * **Spectral peaks:** this feature was defined by [PERSON] [8] as constellation maps that show the most relevant energy bin components in the time-frequency signal representation. Hence, spectral peaks is an attribute that shows high robustness to possible signal distortions (low signal-to-noise ratio (SNR)-see [PERSON] [49], equalization, coders, _etc._) being suitable for robust recognition applications. This feature has been used for automatic music retrieval (e.g., the well-known Shazam search engine by [PERSON] [8]), but also for robust speech recognition (see [PERSON] _et al._[75]). * **MPEG-7 spectrum envelope and normalized spectrum envelope:** the audio spectrum envelope (ASE) is a log-frequency power spectrum that can be used to generate a reduced spectrogram of the original audio signal, as described by [PERSON] _et al._[76]. It is obtained by summing the energy of the original power spectrum within a series of frequency bands. Each decibel-scale spectral vector is normalized with the RMS energy envelope, thus yielding a normalized log-power version of the ASE called normalized audio spectrum envelope (NASE) ([PERSON] _et al._[76]). ASE feature has been used in audio event classification [76], music genre classification ([PERSON] _et al._[77]) and environmental sound recognition (see [PERSON] and [PERSON] [47], or [PERSON] and [PERSON] [48]). * **Stereo panning spectrum feature:** or SPSF for short, this feature provides a time-frequency representation that is intended to represent the left/right stereo panning of a stereo audio signal ([PERSON] _et al._[78]). Therefore, this feature is conceived with the aim of capturing relevant information of music signals, and more specifically, information that reflects typical postproduction in professional recordings. The additional information obtained through SPSF can be used for enhancing music classification and retrieval system accuracies ([PERSON] _et al._[79]).