## 1 Introduction  
Environmental sound classification is a sound recognition system that identifies sound events in the real world, identified as sound event recognition. In recent years, sound problems have received noticeable attention, with popular applications from crime detection [1] to environmental context-aware processing [1], healthcare [2], recognition of automatic speech [3], music information [4], noise mitigation [5], music classification [6], and smart audio-based surveillance systems [7; 8]. Most environmental classification approaches depend on hand-crafted features such as typical automatic classification systems or mid-level representations [6], which obtain a good trade-off between model accuracy and its computational cost [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Environmental sound taxonomy usually has two components: acoustic and classification characteristics. The environmental sound classification is intended to identify the type of event representing a specific sound [23]. For an urban sound event, several typical sound characteristics like mel-frequency coefficients [24], zero-crossing [25], and wavelet transformation [26] have been used as sound features representation. Many classifiers are commonly applied in sound-related classification problems, for instance, support vector machines, extreme learning machines, and Gaussian mixture, in addition to standard machine learning algorithms such as k-nearest neighbors [27; 28; 29; 30]. Nevertheless, traditional techniques are designed to model fewer variations, leading to a lack of frequency and time [29]. The neural network has been the primary choice for environmental recognition, and has been superior to conventional classifiers in the last few years [13]. It proved its efficiency to solve complex categorizing problems compared to traditional classifiers. Convolution neural networks (CNNs) are considered as an appropriate technique for signal tasks due to their ability to capture time and frequency features [31], and a suitable choice for spectrogram-like inputs data [32]. CNNs architecture has three types, which are 1D, 2D, and 3D CNN [33]. The 1D CNN is often used for sequence data processing [34] and was designed to process data in multiple signals such as audio spectrogram [35; 36], 2D CNN is mainly used for image and text recognition [35], and 3D CNN is essentially applied to video recognition and medical applications [37]. Recently, CNN has been applied to the analysis sensor, time series, and periodical data [34; 38; 39]. It has had a huge influence on many audio and music processing tasks using sensor data, periodic signal analysis [34], and diverse acoustic environments data [40; 41]. Furthermore, it has significant outcomes on several tasks in the various fields such as audio classification of video clips [42], genre classification [6], music tagging [43], speech identification [44], large-scale video clip classification [42], speaker identification [44], and environmental sound classification [45; 46; 47; 48; 32; 49]. Although using 1D CNNs in environmental sound classification tasks has substantially improved, they still have a long way compared to 2D CNN based image classification algorithms. 1D CNN is similar to a traditional neural network; however, instead of hand-crafted features, it usually takes the raw signal data [34]. Then, input data is processed through several convolution layers to learn both (low and high) representation levels. Three different approaches have been proposed to apply CNNs on sound event recognition: (i) logmel-CNN [45], where CNN is used as a classifier, and log-mel features as the input; (ii) raw-CNN [50], where CNN extracts audio features from raw waveforms and classifies them without the need for feature engineering; and (iii) hybrid-CNN [49], which uses an average fusion method to combine both raw-CNN and logmel-CNN approaches.