Ensemble of convolutional neural networks to improve animal audio classification

Nanni, Loris; Costa, Yandre M. G.; Aguiar, Rafael L.; Mangolin, Rafael B.; Brahnam, Sheryl; Silla, Carlos N.

doi:10.1186/s13636-020-00175-3

EURASIP Journal on Audio, Speech, and Music Processing

Table 1 Acoustic and visual handcrafted features

From: Ensemble of convolutional neural networks to improve animal audio classification

Features	Descriptors	Reference
Acoustic	Statistical Spectrum Descriptors (SSD) is a set of statistical measures that describe audio content taken from the moments on the Sonogram (the Sone) of each of the twenty-four critical bands defined according to the Bark scale.	[49]
	Rhythm Histogram (RH) is a feature set where the magnitudes of each modulation frequency bin of the twenty-four critical bands defined according to the Bark scale are summed up to form a histogram of “rhythmic energy” per modulation frequency.	[49]
	Modulation Frequency Variance Descriptor (MVD) is a 420-dimensional feature vector that measures variation over the critical frequency bands for each modulation frequency.	[49]
	Temporal Statistical Spectrum Descriptor (TSSD) is a feature set that incorporates temporal information from the SSD (timbre variations, changes in rhythm, etc.).	[14, 44]
	Temporal Rhythm Histograms (TRH) is a feature set that captures rhythmic changes in music over time.	[49]
Visual	The multiscale uniform local binary pattern (LBP).	[41]
	The multiscale LBP histogram Fourier descriptor (LHF) obtained from the concatenation of LBP-HF.	[63]
	The multiscale rotation invariant co-occurrence of adjacent LBPs (LBP-RI).	[40]
	The Multiscale Local Phase Quantization (MLPQ).	[42]
	Ensemble of LPQ, where different configurations of LPQ are examined.	[35]
	The Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor that is applied to heterogeneous dense features maps.	[47]
	Ensemble of variants of the LHF.	[34]
	The Gabor filter feature extraction method where several different values for scale level and orientation are experimentally evaluated.	[17]
	Extracts the standard Binarized Statistical Image Features (BSIF) by projecting subwindows of the entire image onto subspaces.	[24]
	Adaptive hybrid pattern (AHP), which is an LBP variant that is noise robust because a quantization algorithm is applied that uses an equal probability quantization to maximize partition entropy.	[65]
	Locally Encoded Transform feature histogram (LETRIST) that explicitly encodes the joint information within an image across feature and scale spaces.	[54]
	CodebookLess Model, which is a dense sampling approach similar to Bag of Features (BoF).	[60]

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