## 2 Related Work  
The research community has paid more attention to the environmental sound recognition problems with the popularity of CNNs, and its application that exceeds conventional methods for different categorization problems [36; 45]. Deep learning [34; 35] is a particular technique of machine learning that is characterized by its hierarchical multi-level architecture with subsequent phases in data processing [35]. It can extract and learn features directly from raw waveform signals that have efficiently proven several problems in language and images recognition [55; 56]. CNNs are widely applied as a modeling algorithm for multi-variable time-series input data due to the challenges in hand-crafted feature-based methods [32; 45; 46; 49; 57]. However, most of these methods require the input data to be transformed into a 2D representation and feed the input to CNNs architecture as AlexNet and VGG, which were developed initially for object recognition problems [58; 59; 60]. According to many studies, CNNs have fewer parameters to optimize than other unidirectional, and deep neural networks [61]. Deep learning methods, particularly CNNs, are easier to train, unlike other traditional methods, since they provide a structure that performs features extraction and classification in one single block instead of hand-crafted features and classification separately. Many applications consider the log-mel feature of audio signals as an effective audio recognition function. It calculates each sound frame and its magnitude of each frequency area. Each frame's features can be arranged alongside to time axis to create a features map [62]. That makes convolution layers in CNNs to be used as a classifier with the log-mel feature map. This method was first applied and proposed by [PERSON] et al. (2015) in the environmental sound classification [45]. They attempted to get the delta-log-mel and log-mel features extracted from each frame and then used the delta and two-dimensional static log-mel features map, and then they used two-channel CNN for classification. Similarly, [PERSON] (2017) [32] used a two-channel network classification technique with log-mel features map. They modified the network structure that consists of three convolution layers and one fully-connected layer. Besides, they utilized data augmentation techniques to increase training data variation in order to train the network efficiently, which enhanced the classification accuracy by 6%. In the same year, [PERSON] et al. (2017) [63] introduced various deep convolutional models that consist of residual learning and utilize down-sampling and batch normalization technique in the CNN initial layers, which achieved an accuracy of 72% on the UrbanSound8k dataset. On the one hand, many researchers attempt to automatically learn features from raw waveforms [64; 65]. However, that could not outperform the classification accuracy obtained by log-mel features [49]. For instance, [PERSON] et al. (2017) [58] experiments achieved an average accuracy of 92.5% by using mel-frequency, spectrogram, and cross-recurrence alongside AlexNet and GoogLeNet as classifiers with 2D input representations. Its efficiency in summarizing waveforms with high-dimensional spectrograms into a compact representation is one of the primary benefits of using 2D representations. However, they rely on large amounts of the dataset to learn without overfitting, making sound classification modeling difficult using two-dimensional CNNs [47]. 1D CNNs have been widely used in major engineering applications, especially focused on signal processing. Their state-of-the-art performance is highlighted and verified with their unique properties [34]. There are still limited studies in end-to-end environmental research. [PERSON] et al. (2015) [50] found that the time difference between channels has been found to indicate the input position based on the creation of an end-to-end multi-channel 1D CNN for audio recognition with an error rate or 27.1% for single-channel.