# Hands-On Fundamentals of 1D Convolutional Neural Networks--A Tutorial for Beginner Users
## 1 Introduction
Another interesting example the real-time processing capability with significantly lower delay achieved by using 1D CNNs has been presented to classify cardiac acoustic signals for heart anomaly detection. This method proved to be robust and accurate also in reducing the false alarms [75]. The ability of 1D CNNs to learn models from 1D signals such as acoustic signals has also been well demonstrated. Specifically, 1D CNNs are increasingly being used to process raw audio waveforms for environment sound [76], music genre [77], and in general audio [78] classification, along with speech acoustic modeling [79], garnering growing attention in these areas. Given that 1D CNNs can process any 1D signal, they are also applicable to physiological signals such as electroencephalograms (EEG) and electrocardiograms (ECG). Therefore, fields traditionally dominated by 2D CNNs, such as medicine and biology, could benefit from the application of 1D CNNs. A significant portion of the literature on classification problems involves time series signals as input data, with outputs typically predicting labels related to brain states for tasks such as identifying epileptic episodes [80; 81], establishing communication paths between brain processes and external devices [82], and classifying sleep phases [83]. Additionally, there are notable examples of classification of various cardiac disease from ECGs [84; 85; 86; 87], and or other conditions, including Parkinson's disease [88], COVID-19 [89], and diabetes [90]. Moreover, 1D CNNs have likewise become popular for improving intrusion detection in cyber-physical systems [91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102], and, within industrial contexts, particularly for fault and anomaly detection in complex production environments [103; 104]. Additionally, 1D CNNs have proven valuable for cognitive tasks, such as classifying emotional states, including speech emotion recognition. They have been shown to be effective for speaker-independent types of emotion recognition [105], offering increased accuracy while maintaining reasonable computational costs [106; 107]. Noteworthy applications of 1D CNNs extend to the financial sector, where they can be used to identify line chart patterns from financial time series [108]. In spectroscopy, 1D CNNs have demonstrated intriguing applications, such as predicting Dry Matter Content (DMC) from infrared absorbance spectra of fruits--a crucial indicator of fruit maturity that impacts harvest timing.--[109], and classifying corn seed viability [110]. Moreover, in the biomedical field, 1D CNNs have been effectively used to extract lifetime parameters from fluorescence lifetime imaging microscopy signals [111]. In agricultural environmental monitoring, 1D CNNs have been applied to classify soil texture using hyper-spectral data [112; 113; 114] and to predict various soil properties from visible and infrared spectra [115]. In geology, 1D CNNs have demonstrated their ability to link multivariate geochemical data with known mineral deposit locations, significantly enhancing the precision of exploration areas [116]. They also offer rapid seismic predictions, potentially addressing the limitations of traditional physics-informed systems [117], and can be used to classify seismic signals from different tectonic settings [118]. While this literature review is not exhaustive, it highlights that 1D CNNs continue to be widely utilized within the community. This raises the question of whether their prevalent use, despite the availability of potentially more efficient architectures, is due to a deep understanding of their fundamental mechanisms by researchers or the convenience of using readily available open-source software packages. Although many applications have already been developed and more are on the horizon, there appears to be a gap.