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ʼा ी य Ůी Bी िीगक स™ीान िीदWी
NATIONAL INSTITUTE OF TECHNOLOGY DELHI
Department of Electrical Engineering
Project Synopsis
Student & Supervisor Details
Name of Student
Roll Number
Course (Branch),
Semester
T
Devansh Sahney, Anuj Kumar
231230020, 231230013
B.Tech. in electrical engineering
Supervisor’s Name
Project Code-EEPB257
Prof. Ujjwal Kumar Kalla
Project Details
Title of the Project AI-Powered Audio Denoising System for Post-Processing of Noisy Voice
Base Paper (Main
Reference Paper)
Brief Introduction
Motivation of
Project
Y. Luo and N. Mesgarani, "Conv-TasNet: Surpassing Ideal Time–
Frequency Masking for Speech Separation," IEEE/ACM Trans. Audio
Speech Lang. Process., vol. 27, no. 8, pp. 1256–1266, 2019.
A. Pandey and D. Wang, "A New Framework for Supervised Speech
Enhancement in the Time Domain," IEEE/ACM Trans. Audio Speech
Lang. Process., vol. 27, no. 7, pp. 1179–1188, 2019.
A. Defossez et al., "Real Time Speech Enhancement in the Waveform
Domain," Proc. Interspeech, 2020.
This project aims to develop an AI-powered noise filtering system that
improves the quality of recorded voice signals by eliminating background
noise in a post-processing environment. The system is implemented using a
custom-trained 1D convolutional autoencoder inspired by Conv-TasNet,
trained on noisy-clean audio pairs. The final application includes a Gradiobased web interface for simple drag-and-drop usage, making it highly
accessible for content creators, educators, and researchers.
Many voice recordings, including podcasts, lectures, and interviews, suffer
from ambient noise that reduces clarity.
Traditional tools for denoising are complex, expensive, or unavailable to
students and creators.
The motivation is to build a tool that anyone can use, without needing realtime audio processing hardware.
Related Work
(Literature Review
with reference)
Research Gap
•
Most models are designed for real-time noise suppression in hearing
aids or calls.
There’s a lack of lightweight tools designed for offline post-processing
that are easy to use, customizable, and effective for creators working
with recorded audio.
Most existing tools rely on pre-trained models; training a custom
model offers more control and flexibility.
•
•
Objective
Tentative Work
Plane/
Methodology
To train a custom audio denoising model using 1D convolutional neural
networks.
To design a post-processing system that enhances speech clarity in
recorded audio files.
To create a usable interface with Gradio for broad accessibility.
To test performance under various noise conditions and audio types.
•
Study the Conv-TasNet architecture and understand its signal processing
pipeline.
Prepare a dataset for supervised training using noisy-clean audio pairs.
Design a custom 1D convolutional autoencoder model inspired by ConvTasNet.
Implement and train the model using PyTorch with appropriate loss
functions (e.g., SI-SNR loss).
Evaluate model performance using metrics like SNR and PESQ.
Integrate the trained model into a Python application with Gradio for
interface.
Process audio in overlapping chunks for smoothness and consistency.
•
•
•
•
•
•
References (In
IEEE format)
Conv-TasNet (2019) introduced a time-domain speech separation method
outperforming spectrogram-based models.
Time-domain speech enhancement frameworks using CNNs and
autoencoders have shown high performance for offline speech cleanup.
Waveform-domain models have been shown to preserve more natural
speech characteristics during enhancement.
1. Y. Luo and N. Mesgarani, "Conv-TasNet: Surpassing Ideal Time–
Frequency Masking for Speech Separation," IEEE/ACM Trans. Audio
Speech Lang. Process., vol. 27, no. 8, pp. 1256–1266, Aug. 2019.
2. A. Pandey and D. Wang, "A New Framework for Supervised Speech
Enhancement in the Time Domain," IEEE/ACM Trans. Audio Speech
Lang. Process., vol. 27, no. 7, pp. 1179–1188, Jul. 2019.
3. A. Defossez, G. Synnaeve, and Y. Adi, "Real Time Speech Enhancement
in the Waveform Domain," in Proc. Interspeech, 2020, pp. 3291–3295
Signature of Student with Date
Signature of Supervisor with Date
0
