## 6 Conclusions  
A real-time streaming hardware feedforward ANC system was implemented in an FPGA, for in-ear headphones, by using a deep-learning model. The effectiveness of the conventional FxLMS algorithm degrades in in-ear headphones because the electrical-processing delay is long, compared to the short acoustic propagation delay, due to the small size of the headphones. The system presented here uses an enhanced prediction of future data, by using a 10-layer dilated CNN model, with the input receptive field of 327 ms, and, as a result, achieves better noise-power reduction than the FxLMS algorithm. The CNN model has 3232 trained parameters (16-bit floating point). The feedforward ANC consists of an internal speaker and two microphones, the external reference microphone and the internal error microphone; an electrical processing circuit accepts the output signal of the reference microphone, generates an anti-noise signal, and sends it to the internal speaker, to cancel noise. To maintain numerical stability for training, the transfer functions of the primary path and the secondary path are added to the CNN model; the primary path refers to the acoustic propagation path, from the reference microphone input to the error microphone input, and the secondary path includes the sum of the electrical signal path of the electrical processing circuit and the acoustic propagation path, from the internal speaker to the reference microphone input. The transfer function of acoustic propagation paths changes with the change of the incident direction of noise. A 60 h sample of daily noise, acquired from YouTube, and a single-tone sine wave with frequency <2000 Hz are combined to form the input data for the training of the CNN model; the incident direction of the input data was chosen to be 0\({}^{\circ}\) or 90\({}^{\circ}\), with equal probability. The CNN model was implemented in an FPGA that had a system clock frequency of 120 MHz; a 16 \(\times\) 16 systolic array was used for speedup. The operation of the feedforward ANC system was demonstrated, successfully, in a real application scenario, by combining the FPGA, an analog board, and an in-ear headphone. The measured system latency was 170.6 \(\upmu\)s. For a band-limited pink noise input (100\(\sim\)2000 Hz), the noise-power reduction was measured to be 14.8 dB and 14.3 dB at 0\({}^{\circ}\) and 90\({}^{\circ}\) incident direction, respectively, and the attenuation bandwidth was 2000 Hz, at both incident directions.  
**Author Contributions:** Conceptualization, [PERSON] and [PERSON]; hardware design, [PERSON]. (analog/digital whole design), J.P. (16 \(\times\) 16 systolic array), and W.-C.L. (USB 2.0 link); software, [PERSON]-[PERSON]; smartphone program, W.-C.L.; project administration (working together during the whole editorial process of the manuscript), [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by Samsung Electronics Co., Ltd. (IO201209-07912-01); in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2019R1A5A1027055); and in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2022R1A2C2003451).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.