## 1 Introduction
It can be observed that most of the crackles are located in the intervals [0–0.7], [3; 1–3; 6], [6–6; 6; 4] and [9.4–9.7] seconds. For many years, signal processing and machine learning approaches have been combined for event detection and classification tasks using spectro-temporal features [14; 15; 16]. Specifically for the task of crackle sound detection, several approaches have been proposed based on spectrogram analysis [17; 18], autoregressive (AR) models [19; 20], wavelet transform [21; 22; 23; 24], fractal dimension filtering [25; 26; 27; 28], entropy [29; 30], empirical mode decomposition (EMD) [31], fuzzy systems [32], Gaussian mixture models (GMM) [33], logistic regression [34], support vector machines (SVM) [35; 36; 37], independent component analysis (ICA) [38], multi-perceptron networks (MPNs) [39], non-negative matrix factorization (NMF) [40], convolutional neural networks (CNNs) [41; 42], recurrent neural networks (RNNs) [43; 44] and hybrid neural networks [45; 46]. [PERSON] et al. [23] detected crackle sounds testing different windows and wavelets for time-scale analysis. The extracted features were fed into SVM and k-nearest neighbor (kNN) models, but frequency bands containing no crackle information were removed using a dual-tree complex wavelet to reduce redundancy. [PERSON] and [PERSON] [35] developed a crackle detection method using preprocessing, feature extraction (ratio between frequency of limbic signal, standard deviation of the limbic signal time, and the smoothing time of the limbic signal) and classification based on SVM. In [43], an event detection approach for crackle detection, based on RNNs using spectral features, was proposed. The approach exploited spectral information and temporal dependencies of the lung sounds, showing robustness regarding the contamination of the lung sound recordings with noise, bowel, and heart sounds. [PERSON] et al. [38] proposed to evaluate the efficiency of three popular ICA algorithms (FastICA, information maximization, and temporal decorrelation source separation) to determine the best method to extract crackle sounds. Information maximization achieved the best results, determining the presence of crackles in the independent components by means of their kurtosis and skewness, whereas the type of crackle was indicated by their characterization via the spectrogram of those components. In [42], CNNs were used to recognize lung sounds (including coarse and fine crackles) by means of spectrograms, Mel frequency cepstrum coefficient (MFCC) features and local binary pattern (LBP) features. Results demonstrated that the performance depends on learning parameters, batch size, and of iterations, band moreover, CNNs can replace conventional classifiers using fully connected layers to train the previous features. In [24], a classification system was proposed based on wavelets, genetic algorithms, and SVMs in order to discriminate between the presence or absence of crackles caused by pneumonia. Recently, [PERSON] and [PERSON] [28] proposed a crackle separation and detection technique combining an iterative envelope mean (IEM) method with the established fractal dimension (FD) technique [47]. The IEM method estimated the non-stationary and stationary parts of the lung sound signal and then the FD technique was applied to the estimated non-stationary output of the IEM method for further removing elements related to normal breath sounds. Authors indicated that IEM-FD filtering showed a high rate of crackle detectability when several signal-to-noise ratios (SNRs) were evaluated. Crackle detection is a complex task due to factors such as: (a) nonstationarity of lung and crackle sounds, (b) low-magnitude relation between crackles and lung sounds (SNR), (c) crackle overlapping, (d) crackle waveform distortion by lung sounds, and (e) difficulty to establish the time domain parameters such as \(t_{IDW}\), \(t_{2 CD}\), and \(t_{LDW}\).