Authors :
V. Sudha Rani; Dr. A. N. Satyanrayana; Aroju Santhosh; Maliha; Erravelly Sricharan
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/mtzm7rfm
Scribd :
https://tinyurl.com/mr22syrr
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2694
Abstract :
A comprehensive study is conducted to enhance
audio quality in challenging noisy environments, departing from
conventional approaches that target specific sound components.
This paper focuses on a modified U-Net architecture integrat-
ing broader audio features and implementing a probabilistic
framework for direct spectral content reconstruction. Multiple
variants of this system were rigorously tested across diverse
noise levels and reverberation conditions, with performance
evaluation conducted using objective metrics such as SDR,
signal-to-noise ratio, evaluation of voice, and intelligibility
scores.
The paper demonstrates that proposed enhanced U-Net
architecture, characterized by strategically designed
connections within its structure, consistently outperforms
traditional audio enhancement methods across a range of noise
scenarios. Notably,the improvements in audio quality were most
pronounced in highly reverberant environments, where
conventional techniques often struggle to deliver satisfactory
results. These results high- light the effectiveness of our novel
approach in significantly enhancing audio fidelity and
intelligibility, particularly in real- world noisy conditions.
Keywords :
Audio Enhancement, Noisy Environments, U-Net Architecture, Spectral Content Reconstruction, SDR, SNR.
References :
- F. Rund, V. Vencovsky, and M. Semansk ´ y, “An evalu-ation of click detection algorithms against the results of listening tests,” J. Audio Eng. Soc., vol. 69, no. 7/8, pp. 586–593, July/Aug. 2021.
- H. T. de Carvalho, F-R. Avila, and L. W. P. Biscainho, “Bayesian restoration of audio degraded by low frequency pulses modeled via Gaussian process,” IEEE J. Selected Topics Signal Process., vol. 15, no. 1, pp. 90–103, Oct. 2021.
- J. Berger, R. R. Coifman, and M. J. Goldberg, “Removing noise from music using local trigonometric bases and wavelet packets,” J. Audio Eng. Soc., vol. 42, no. 10, pp. 808–818, Oct. 1994.
- P. A. A. Esquef, “Audio restoration,” in Handbook of Signal Processing in Acoustics, pp. 773–784. Springer, New York, NY, USA, 2008.
- S. Boll, “Suppression of acoustic noise in speech using spectral subtrac- tion,” IEEE Trans. Acoust. Speech Signal Process., vol. 27, no. 2, pp. 113–120, Apr. 1979.
- S. J. Godsill and P. J. W. Rayner, Digital Audio Restoration - A Statistical Model Based Approach, Springer, 1998.
- Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error log- spectral amplitude estimator,” IEEE Trans. Acoust. Speech Signal Process., vol. 33, no. 2, pp. 443–445, Apr. 1985
A comprehensive study is conducted to enhance
audio quality in challenging noisy environments, departing from
conventional approaches that target specific sound components.
This paper focuses on a modified U-Net architecture integrat-
ing broader audio features and implementing a probabilistic
framework for direct spectral content reconstruction. Multiple
variants of this system were rigorously tested across diverse
noise levels and reverberation conditions, with performance
evaluation conducted using objective metrics such as SDR,
signal-to-noise ratio, evaluation of voice, and intelligibility
scores.
The paper demonstrates that proposed enhanced U-Net
architecture, characterized by strategically designed
connections within its structure, consistently outperforms
traditional audio enhancement methods across a range of noise
scenarios. Notably,the improvements in audio quality were most
pronounced in highly reverberant environments, where
conventional techniques often struggle to deliver satisfactory
results. These results high- light the effectiveness of our novel
approach in significantly enhancing audio fidelity and
intelligibility, particularly in real- world noisy conditions.
Keywords :
Audio Enhancement, Noisy Environments, U-Net Architecture, Spectral Content Reconstruction, SDR, SNR.