## 1 Introduction  
Absorption spectroscopy in the terahertz (THz) frequency region (0.1-10 THz) allows for the identification and quantitative detection of gas-phase polar molecules, including many volatile organic compounds (VOCs). Molecules of interest can be identified via their rich, complex, and unique THz region rotational absorption spectra, which act as fingerprints. Hence, THz absorption spectroscopy and gas sensing can be valuable in many scientific, industrial and environmental applications [1, 2, 3, 4, 5]. Moreover, recent advances in microelectronic THz sources and detectors has increased the viability of portable, miniature spectrometers capable of sensing a wide variety of gas molecules which are active in the THz region [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. Similar to other spectroscopic techniques, rotational spectra in the THz frequency region require signal/data processing for spectral interpretation, gas species identification, and quantitative gas sensing. Spectral identification is often difficult because of overlapping transitions, among a single molecular spectra or for a complex gas mixture, which is further complicated by collisional broadening at realistic pressures found in applications. Identification of rotational fingerprints with high accuracy, sensitivity, and selectivity is a challenge that can be addressed and automated with artificial intelligence (AI) and machine learning (ML) methods. Automated spectral identification using ML requires a model that can learn to identify unique spectral features that provide fingerprints for each molecule and separate those features from similar features of other molecules. Ideally, a ML \"artificial spectroscopist\" would make spectral classification decisions based on identification of unique fingerprints within a spectra just as a human spectroscopist would, while ML methods, including shallow neural networks composed of multi-layer perceptrons, have been demonstrated to identify spectra of different molecules from THz absorption spectroscopy [17], IR and Raman spectroscopy [18; 19], and excitation spectroscopy [20], their black box nature, and the difficulty of interpreting the logic behind their decision making, can lead to an erosion of acceptance and trust in such models [21], especially in industrial applications. Thus, there is a need for AI-based models to not only have high accuracy, sensitivity, and selectivity, but also to have good interpretability. AI/ML based classification generally requires data with good descriptive and variable features. Often, the original raw data is reduced to derived values which are non-reducible, while preserving the most useful and distinctive information, in a process known as feature extraction. Since their resurgence in the late 2000s, neural networks have facilitated the training of complex models capable of wide variety of tasks such as image recognition, segmentation, classification, denoising, representation learning, and computer vision [22; 23; 24; 25; 26; 27]. Deep networks are able to learn patterns in data by extracting good features. Particularly, convolutional neural networks (CNN) are widely used to extract features from data with the help of convolutional and subsampling or pooling layers [22]. These features are used to represent the data in a compact and useful form. The most common CNNs are two dimensional (2D), since they are often used on image data [23] and numerous works have demonstrated 2D CNNs for learning useful representations such as edges, contours, contrast, saturation etc. However, 2D CNNs are difficult to use to interpret spectral data because spectral data is highly-dimensional along a single axis. On the other hand, 1D CNNs, widely used to learn time-series data for classification and regression purposes [22; 28], are suited for data that is highly-dimensional along a single axis. A 1D CNN moves a sliding window filter kernel along a single axis to learn features from the data (spectrum).