## 4 Physical Audio Features Extraction Techniques
### Wavelet-Based Physical Features
* **MP-based Gabor features:** [PERSON] _et al._[130] proposed the construction of multiresolution Gabor dictionaries appropriate for audio signal analysis, which is applied for music and speech signals observed in noise, obtaining a more efficient spectro-temporal representation compared to a full multiresolution decomposition. In this work, Gabor atoms are given by time-frequency shifts of distinct window functions. [PERSON] _et al._ describe in [131] the use of 2D Gabor filterbank and illustrate its response to different speech phenomena such as harmonicity, formants, vertical onsets/offsets, noise, and overlapping simultaneous speakers. [PERSON] and [PERSON] propose in [132] the use of spectro-temporal Gabor features to enhance automatic speech recognition performance in adverse conditions, and obtain better results when Hanning-shaped Gabor filters are used in contrast to more classical Gaussian approaches. [PERSON] _et al._[14] proposed using MP and a dictionary of Gabor functions to represent the time dynamics of environmental sounds, which are typically noise-like with a broad flat spectrum, but may include strong temporal domain signatures. Coupled with Mel Frequency Cepstral Coefficients (see Section 5.6 for more details), the MP-based Gabor features allowed improved environmental sound recognition. In [133], [PERSON] _et al._ proposed a nonuniform scale-frequency map based on Gabor atoms selected via MP, onto which Principal Component Analysis and Linear Discriminate Analysis are subsequently applied to generate the audio feature. The proposed feature was employed for environmental sound classification in home automation. * **Spectral decomposition:** in [134], [PERSON] _et al._ proposed an audio feature extraction scheme applied to audio effect classification and based on spectral decomposition by matching-pursuit in the frequency domain. Based on psychoacoustic studies, a set of spectral sinusoid-Gaussian basis vectors are constructed to extract pitch, timbre and residual in-harmonic components from the spectrum, and the audio feature consists of the scales of basis vectors after dimension reduction. Also in [135], [PERSON] _et al._ applied an Adaptive Time Frequency Transform (ATFT for short) algorithm for music genre classification as a Wavelet decomposition but using Gaussian-based kernels with different frequencies, translations and scales. The scale parameter, which characterizes the signal envelope, captures information about rhythmic structures, and it has been used for music genre identification (see [PERSON] [13]). * **Sparse coding tensor representation:** this work presents an evolution of Gabor atom MP-based audio feature extraction of [PERSON] _et al._[14]. The method proposed in the work by [PERSON] and [PERSON] [136] tries to preserve the distinctiveness of the atoms selected by the MP algorithm by using a frequency-time-scale tensor derived from the sparse coding of the audio signal. The three tensor dimensions represent the frequency, time center and scale of transient time-frequency components with different dimensions. This feature was coupled with MFCC and applied to perform sound effects classification.