## 4 Conclusions  
With the advancement of technology, the level of quiet demanded by people in places such as living environments and vehicles has increased. The performance requirements for sound-absorbing materials in products are increasing, and the number of electronic/mechanical systems with ANC technology is rapidly increasing. In particular, artificial intelligence is a promising technology for maximizing noise-cancellation performance.  
In this study, LSTM networks were proposed for the ANC algorithm and applied to diesel engine sounds. The LSTM is a recurrent neural network for managing long  
Figure 7: ANC simulation result using FxLMS and LSTM for the impulsive noise signal measured from the start-up of the diesel engine. (**a**) Time data of diesel engine noise and ANC results; (**b**) short-time Fourier transform analysis of original noise and noise residue; (**c**) power spectral density of the diesel engine noise and ANC result in the period when the error signal is minimized.  
sequence data. The ANC controller based on the LSTM layers was suitable for predicting the reference noise and capable of generating the control sounds to minimize the noise residue. The structure of the LSTM neural networks was presented, and the approach to training the LSTM controller for ANC was described. Simulations were conducted to validate the performance of the LSTM controller. Noise samples were acquired from a single-cylinder diesel engine, and the reference noises selected included single harmonics, superposed harmonics, and impulse sounds generated from the diesel engine. For each reference sound, ANC was performed by applying the conventional FxLMS and proposed algorithms. In ANC simulation results, the controller comprising the proposed LSTM neural networks showed performance improvements in terms of convergence and accuracy relative to the conventional FxLMS. In particular, the ANC using the LSTMs for the steady-state harmonic noise converged faster than the FxLMS system. Based on this excellent convergence rate, the overall noise level was significantly reduced. Through the ANC for the superposed harmonics, the proposed algorithm was verified as having excellent performance, even for broadband noise. In particular, the proposed algorithm can control transient sounds. The conventional FxLMS has a disadvantage in that the filter must be re-converged as the signal is modulated. The proposed algorithm showed fast convergence even for the modulated sounds because the training had already been completed. The LSTM network has the advantage of high-frequency noise and transient noise control because it immediately predicted an output when only a fixed number of data was entered. Therefore, the proposed method conducted noise cancellation with robustness in various severely noisy situations, such as in the context of broadband and impulse sounds. Based on these results, LSTM neural networks are considered an appropriate approach to ANC in practical situations.  
Conceptualization, S.K. and J.P.; investigation, S.K. and B.-S.K.; methodology, S.K.; formal analysis, B.-S.K.; project administration, J.P.; resources, J.P.; software, S.K.; supervision, J.P.; writing--original draft, S.K. and B.-S.K.; writing--review and editing, S.K. and J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry (IPET) through Eco-friendly Power Source Application Agricultural Machinery Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322047-5).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest.