Clicks classification of sperm whale and long-finned pilot whale based on continuous wavelet transform and artificial neural network

Seasonal Variation of Captive Meagre Acoustic Signalling: A Manual and Automatic Recognition Approach

“…The specific objectives were to (i) create a library of calls; (ii) create an automated recognition system to detect and classify meagre calls and apply this to the acoustic files recorded between January and July; (iii) compare results between the automatic and the manual approach; and (iv) investigate diel and seasonal patterns of calling activity, namely in relation to the breeding season. [50] S ANN e [51] Sea lions I ANN d [52] ANN, artificial neural network; C, call type; GMM, Gaussian mixture model; HMM, hidden Markov model; I, individual; KNN, K-nearest neighbours; LPCC, linear prediction cepstral coefficients; MFCC, Mel-frequency cepstral coefficients; MRAF, multiresolution acoustic features; S, species; SCF, spectrogram correlator filter; Sparse, Sparse classification; SPL, sound pressure level; SVM, support vector machine; a vector composed of several sound coefficients/parameters; b each vocalization was characterized by its simultaneous modulations in duty cycle and peak frequency; c features were selected using a local discriminant basis; d average logarithmic spectrum on the backpropagation network input layer; e a wavelet coefficient matrix, plus a frequency features and time feature; f SPL feature-based signal detector using a correlation coefficient to measure the matching with the training selected data; g a contour-based classifier that applies a number of noise-cancellation techniques to a spectrogram and then searches for connected regions of data which rise above a pre-determined threshold; h a generalized tonal sound detector for extracting representative frequencies of delphinid whistles; i cepstral coefficient features with first and second derivatives, unpredictability measure feature and MUSIC algorithm feature.…”

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

“…In order to verify the performance of EEMD-ICA-WTD, six different denoising methods are used as comparison methods. ey are EMD-TD [33], EMD-WTD [34], EEMD-TD [35], EEMD-WTD [36], wavelet soft threshold denoising (WSTD) [37], and multiband spectral subtraction (MBSS) [38]. To verify the universality of the methods, two kinds of porcine acoustic signals with different SNRins are denoised.…”

Synthesis and Modification of Cetacean Tonal Sounds for Underwater Bionic Covert Detection and Communication

“…This mismatch is caused by the different contour curvatures between the tonal sound and the SFM signal. As can be seen from (10), once the frequency range and the duration of a SFM signal are fixed, neither its contour curvature nor its slope can be changed. To have a close match to the true tonal sounds, based on (10), and referring to the method of changing the contour curvature and contour slope in the PFMB signal model, the instantaneous frequency of a novel SFMB signal model is proposed as follows…”

“…Its main idea is to disguise sonar or communication signals into cetacean sounds. During the identification of underwater monitoring systems, these bionic sonar and communication signals could be classified as ocean noise and filtered out [7]- [10], thereby achieving the purpose of covert ASD and UAC. As an approach with great potential, underwater bionic covert detection and communication has been attracting more and more attentions in recent years [11]- [22].…”