This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Joshi Shriya,
Kendre Rohidas,
Bhima Shankar Kumbhar,
Kulkarni Shruti,
Vaibhav Godase,
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
Abstract
Adaptive noise cancellation (ANC) is an important signal-processing technique for improving speech and sensor signals in environments affected by interference. This paper presents a simulation-based comparison of the least mean squares (LMS) algorithm, normalized least mean squares (NLMS), and a compact deep-learning denoiser. The LMS and NLMS filters are evaluated in a reference-based ANC configuration in which a reference microphone signal is correlated with the interference present in a primary signal. The neural model is evaluated separately as a supervised, frame-based denoiser trained on synthetic paired clean and noisy harmonic signals. The algorithms are compared using signal-to-noise ratio (SNR), waveform inspection, and convergence-related behavior. In the reference-based experiment, the unprocessed input SNR was 0.92 dB, while the LMS residual achieved 10.03 dB and the NLMS residual achieved 5.45 dB under the signal realization and parameters used. In the separate neural-denoising test, average segment of SNR increased from 0.93 dB to 6.50 dB, an improvement of 5.58 db. These values are measured outputs of the included synthetic simulation, not results from a public speech corpus or real-world recording. The experiments are not a direct head-to-head ranking because reference-based ANC and supervised denoising use different input assumptions and test protocols. The findings illustrate that LMS can perform strongly when its step size and reference signal suit the scenario, NLMS offers input-power normalization but still requires parameter tuning, and neural denoising can learn nonlinear mappings at the cost of training data and inference complexity. A reproducible evaluation using recorded speech, multiple noise types, repeated random seeds, and perceptual metrics is proposed for future work.
Keywords: Adaptive Noise Cancellation; Deep Learning; Speech Enhancement; Snr; Adaptive Filtering; Neural Denoising
References
1. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
2. Godase V, Godase J. Diet prediction and feature importance of gut microbiome using machine learning. Evolution in Electrical and Electronic Engineering. 2024 Nov 6;5(2):214-9.
3. Jamadade VK, Ghodke MG, Katakdhond SS, Godase V. A comprehensive review on scalable Arduino radar platform for real-time object detection and mapping. Journal of Microprocessor and Microcontroller Research. 2025 May;2(2):1-2.
4. Godase V. A comprehensive study of revolutionizing EV charging with solar-powered wireless solutions. Advance Research in Power Electronics and Devices e-ISSN. 2025 Apr 18:3048- 7145.
5. Godase V. Advanced Neural Network Models for Optimal Energy Management in Microgrids with Integrated Electric Vehicles. InProceedings of the International Conference on Trends in Material Science and Inventive Materials (ICTMIM-2025) DVD Part Number: CFP250J1-DVD 2025 Apr 18.
6. Dange R, Attar E, Ghodake P, Godase V. Smart agriculture automation using ESP8266 NodeMCU. J. Electron. Comput. Netw. Appl. Math,(35). 2023 Jul:1-9.
7. Godase V. Optimized algorithm for face recognition using Deepface and multi-task cascaded convolutional network (MTCNN). Optimum Science Journal. 2025 May 5(3):66-74.
8. Bhaganagare S, Chavan S, Gavali S, Godase V. Mood mate: Advancing real-time mental health monitoring with multimodal ai. International Journal of Advanced Research in Science, Communication and Technology. 2025:2148-71.
9. Godase V. Navigating the digital battlefield: An in-depth analysis of cyber-attacks and cybercrime. International Journal of Data Science, Bioinformatics and Cyber Security. 2025 Jan 17;1(1):16-27.
10. Godase V, Jagadale A. Three element control using PLC, PID & SCADA interface. International Journal for Scientific Research & Development. 2019;7(2):1105-9.
11. Godase V. Edge AI for smart surveillance: Real-time human activity recognition on low-power devices. International Journal of AI and Machine Learning Innovations in Electronics and Communication Technology. 2025 Jan 18;1(1):29-46.
12. Godase V. LoRaEdge-ESP32 synergy: Revolutionizing farm weather data collection with low- power, long-range IoT. Advance Research in Analog and Digital Communications. 2025 Jan 1.
13. Godase V. Comparative study of ladder logic and structured text programming for PLC. Available at SSRN. 2025 Jul 2.
14. Godase V, Modi S, Misal V, Kulkarni S. Real-time object detection for autonomous drone navigation using YOLOv8,‖. Advance Research in Communication Engineering and its Innovations. 2025;2(2):17-27.
15. Godase V. Smart energy management in manufacturing plants using PLC and SCADA. Advance Research in Power Electronics and Devices. 2025 May 8;2(2):14-24.
16. Godase V. IoT-MCU Integrated Framework for Field Pond Surveillance and Water Resource Optimization. International Journal of Emerging IoT Technologies in Smart Electronics and Communication. 2025 Jan 1.
17. Godase V. Graphene-based nano-antennas for terahertz communication. International Journal of Digital Electronics and Microprocessor Technology. 2025 Aug;1(2):1-4.
18. Godase V, Khiste R, Palimkar V. AI-Optimized reconfigurable antennas for 6G communication systems. Journal of RF and Microwave Communication Technologies. 2025 Aug;2(3):1-2.
19. Bhaganagare S, Chavan S, Gavali S, Godase VV. Voice-controlled home automation with esp32: A systematic review of iot-based solutions. Journal of Microprocessor and Microcontroller Research. 2025 Sep 1;2(3):1-3.
20. Jamadade VK, Ghodke MG, Katakdhond SS, Godase V. A comprehensive review on scalable Arduino radar platform for real-time object detection and mapping. Journal of Microprocessor and Microcontroller Research. 2025 May;2(2):1-2.
21. Godase, V. (2025). Cross-Domain Comparative Analysis of Microwave Imaging Systems for Medical Diagnostics and Industrial Testing. Journal of Microwave Engineering & Technologies, 12(2), 39-48p.
22. Jamadade VK, Ghodke MG, Katakdhond SS, Godase V. A Review on Real-time Substation Feeder Power Line Monitoring and Auditing Systems,”. International Journal of Emerging IoT Technologies in Smart Electronics and Communication. 2025 Sep;1(2):1-6.
23. Godase VV. VLSI-Integrated Energy Harvesting Architectures for Battery-Free IoT Edge Systems. Journal of Electronics Design and Technology. 2025 Sep;2(3):1-2.
24. Salunkhe A, Pawar V, Pise P, Mule S, Survase A, Godase V, Zambre S. A review on real-time RFID-based smart attendance systems for efficient record management. Advance Research in Analog and Digital Communications. 2025 Aug;2(2):32-46.
25. Godase VV. A neuromorphic-inspired, low-power VLSI architecture for edge AI in IoT sensor nodes. Journal of Microelectronics and Solid State Devices. 2025;12(2):41-7.
26. Nagane MS, Pawar MP, Godase PV. Cinematica sentiment analysis. Journal of Image Processing and Intelligent Remote Sensing. 2022 May;2(3):27-32.
27. Akhtar ZB. Artificial intelligence within medical diagnostics: A multi-disease perspective. Artificial intelligence in Health. 2025 Jan 6;2(3):44-62.
28. Wang Z, Huang Y, Song D, Ma L, Zhang T. Promptcharm: Text-to-image generation through multi-modal prompting and refinement. InProceedings of the 2024 CHI conference on human factors in computing systems 2024 May 11 (pp. 1-21).
29. Reis MJ. Deep-Learning-Driven Adaptive Filtering for Non-Stationary Signals: Theory and Simulation. Electronics. 2026 Jan 15;15(2):381.
30. Daniel VD, Mythili C. Active noise cancellation using hybrid CNN-LSTM classification and genetic algorithm-based community detection extraction with LMS noise filtering process. Noise Control Engineering Journal. 2024 Nov 1;72(6):577-94.
31. Kowsalya M, Rajasree SA, Janani S, Sujjit S, Shahith M, Sabarish R. Real-Time Implementation of Adaptive Noise Cancellation using DSP and Machine Learning Algorithm. In2026 7th International Conference on Electronics and Sustainable Communication Systems (ICESC) 2026 Aug 26 (pp. 1870-1875). IEEE.
32. Qassab MS, Ali QI. A Conceptual Study on Active Noise Cancellation: From Adaptive Algorithms to Deep Learning Approaches. ITEGAM-JETIA. 2026 Jun 25;12(59):2061-75.
33. Libina M, Mahan K. Hybrid noise reduction and enhancement of audio quality using deep learning. In2023 Second International Conference on Electronics and Renewable Systems (ICEARS) 2023 Mar 2 (pp. 1131-1135). IEEE.
34. Gupta VK, Mishra AN, Kumar N. AI-Based Adaptive Noise Cancellation for Enhanced Speech and Wireless Communication Systems. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES. 2024 Dec 31;10(2):440-9.
35. Syed A, Ali SH. Implementation of Acoustic Echo Cancellation through Adaptive Filtering Algorithms in MATLAB: Acoustic Echo Cancellation through Adaptive Filtering Algorithms. The Sciencetech. 2025 Mar 31;6(1):86-101.
36. Rajesh A, Abin OK, Vinod A, Krishna A, Unnithan KS, Anoop S. Comparative Analysis of Passive Noise Cancellation Using LMS, NLMS, RLS, and APA Algorithms. In2026 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) 2026 Mar 12 (Vol. 4, pp. 1-6). IEEE.
37. Nagal R, Kumar P, Bansal P. A Survey with Emphasis on Adaptive filter, Structure, LMS and NLMS Adaptive Algorithm for Adaptive Noise Cancellation System. Journal of Engineering Science & Technology Review. 2017 Mar 1;10(2):150.
38. Suresh A, Farhana S, Ali A, Khan A, Sajini T. A Comparison of Noise Cancellation of Speech Signals Using Adaptive LMS Algorithm and Deep Learning Algorithm. International Journal of Advance Research and Innovative Ideas in Education. 2020;6:1625-33.
Current Trends in Signal Processing
| Volume | 16 | |
| 03 | ||
| Received | 19/10/2026 | |
| Accepted | 26/10/2026 | |
| Published | 03/11/2026 | |
| Publication Time | 15 Days |