Voice Extraction from Background Noise using Filter Bank Analysis for Voice Communication Applications

Padmapriya, J.; Sasilatha, T.; Karthickmano, j.R; Bharathi, V. Subbiah

doi:10.1109/icicv50876.2021.9388453

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The use of filter bank analysis for communication applications and focus on features like Loudness, Pitch Intensity, and Timing is mentioned [20]. They note that the features of the speech signal in noisy environments can be reliably extracted through bandpass filtering.…”

Section: Related Workmentioning

confidence: 99%

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

ÖZKURT

2024

Preprint

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This study investigates the effects of audio processing and filtering strategies to enhance the performance of speech recognition systems in noisy environments. The focus is on the Short-Time Fourier Transform (STFT) operations applied to noisy audio files and noise reduction procedures. While STFT operations form the basis for detecting noise and analyzing the speech signal in the frequency domain, noise reduction steps involve threshold-based masking and convolution operations. The results obtained demonstrate a significant improvement in speech recognition accuracy in noisy environments through audio processing and filtering strategies. A detailed analysis of the graphs provides guidance for evaluating the effectiveness of noise reduction procedures and serves as a roadmap for future research. This study emphasizes the critical importance of audio processing and filtering strategies in improving the performance of speech recognition systems in noisy environments, laying a foundation for future studies.

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Section: Related Workmentioning

confidence: 99%

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

ÖZKURT

2024

Preprint

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Signal Processing Methods and Features for Enhancing a Speech Signal

Ganta,

Mittal,

Venkat Vijay Kishore

2023

Algorithms for Intelligent Systems

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G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost

Gupta

Singh²,

Singh

2022

Wireless Pers Commun

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The pandemic caused due to COVID-19, has seen things going online. People tired of typing prefer to give voice commands. Most of the voice based applications and devices are not prepared to handle the native languages. Moreover, in a party environment it is difficult to identify a voice command as there are many speakers. The proposed work addresses the Cocktail party problem of Indian language, Gujarati. The voice response systems like, Siri, Alexa, Google Assistant as of now work on single voice command. The proposed algorithm G-Cocktail would help these applications to identify command given in Gujarati even from a mixed voice signal. Benchmark Dataset is taken from Microsoft and Linguistic Data Consortium for Indian Languages(LDC-IL) comprising single words and phrases. G-Cocktail utilizes the power of CatBoost algorithm to classify and identify the voice. Voice print of the entire sound files is created using Pitch, and Mel Frequency Cepstral Coefficients (MFCC). Seventy percent of the voice prints are used to train the network and thirty percent for testing. The proposed work is tested and compared with K-means, Naïve Bayes, and LightGBM.

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Voice Extraction from Background Noise using Filter Bank Analysis for Voice Communication Applications

Cited by 4 publications

References 10 publications

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

Signal Processing Methods and Features for Enhancing a Speech Signal

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost

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