## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.6. Spectrum Shape-Related Physical Frequency Features  
Another relevant set of frequency features are the ones that try to describe the shape of the spectrum of the audio signal. The following paragraphs describe the most widely employed, and some of the newest contributions in this area. * **Bandwidth:** 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) (see [PERSON] [34]). However, it is difficult to distinguish between complex tonal sounds (e.g., music, instruments, _etc._) from complex noise-like sounds using only this feature. It can be defined over the power spectrum or in its logarithmic version (see [PERSON] _et al._[59], or [PERSON] and [PERSON] [93]) and it can be computed over the whole spectrum or within different subbands (like in [PERSON] and [PERSON] [102]). MPEG-7 defines audio spectrum spread (ASS) as the standard deviation of the signal spectrum, which constitutes the second moment while (being the ASC the first one). Spectral bandwidth has been used for music classification ([PERSON] _et al._[41], [PERSON] _et al._[12], [PERSON] _et al._[42], [PERSON] and [PERSON] [28]), and environmental sound recognition ([PERSON] _et al._[18], [PERSON] and [PERSON] [47], [PERSON] and [PERSON] [48]). * **Spectral dispersion:** this is a measure closely related to spectral bandwidth. The only difference is that it takes into account the spectral center (median) instead of the spectral centroid (mean) (see [PERSON] _et al._[83]). * **Spectral rolloff point:** defined as the 95 th percentile of the power spectral distribution (see [PERSON] and [PERSON] [61]), spectral rolloff point can be regarded as a measure of the skewness of the spectral shape. It can be used, for example, for distinguishing between voiced from unvoiced speech sounds. It has been used in music genre classification (like in [PERSON] and [PERSON] [103], [PERSON] _et al._[41], [PERSON] _et al._[40], [PERSON] _et al._[12], [PERSON] _et al._[42], [PERSON] and [PERSON] [28], [PERSON] _et al._[9]), speech/music discrimination ([PERSON] and [PERSON] [61]), musical instrument classification ([PERSON] _et al._[10]), environmental sound recognition ([PERSON] _et al._[18]), audio-based surveillance systems ([PERSON] _et al._[24]) and music mood classification ([PERSON] _et al._[86]). * **Spectral flatness:** this is a measure of uniformity in the frequency distribution of the power spectrum, and it can be computed as the ratio between the geometric and the arithmetic mean of a subband (see [PERSON] and [PERSON] [102]) (equivalent to the MPEG-7 audio spectrum flatness (ASF) descriptor [45]). This feature allows distinguishing between noise-like sounds (high value of spectral flatness) and more tonal sounds (low value). This feature has been used in audio fingerprinting (see [PERSON] _et al._[104]), musical onset detection ([PERSON] _et al._[57]), music classification ([PERSON] _et al._[105], [PERSON] _et al._[106], [PERSON] and [PERSON] [28]) and environmental sound recognition ([PERSON] and [PERSON] [47], [PERSON] and [PERSON] [48]). * **Spectral crest factor:** in contrast to spectral flatness measure, spectral crest factor measures how peaked the power spectrum is, and it is also useful for differentiation of noise-like (lower spectral crest factor) and tonal sounds (higher spectral crest factor). It can be computed as the ratio between the maximum and the mean of the power spectrum within a subband, and has been used for audio fingerprinting (see [PERSON] _et al._[104], [PERSON] and [PERSON] [103]) and music classification ([PERSON] _et al._[105], [PERSON] _et al._[106]). * **Subband spectral flux:** or SSF for short, this feature is inversely proportional to spectral flatness, being more relevant in subbands with non-uniform frequency content. In fact, SSF measures the proportion of dominant partials in different subbands, and it can be measured accumulating the differences between adjacent frequencies in a subband.