Reduction of noise for speech signal enhancement using Spectral Subtraction method

Saldanha, Jennifer C.; Shruthi, O R

doi:10.1109/infosci.2016.7845298

Cited by 3 publications

(2 citation statements)

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“…that is, we have approximated the true power spectral density of s(n) by its spectral energy. From (7), the output of the Wiener filter in the frequency domain is given by…”

Section: The Implicit Wiener Filtermentioning

confidence: 99%

“…Spectral subtraction (SS) is one of the traditional methods used for enhancing speech degraded by additive stationary background noise [5], [6]. The multi-band spectral subtraction algorithm is proposed in [7] to enhance speech corrupted by fan noise. A modified spectral subtraction algorithm is proposed in [8], where the noise spectrum is updated on the basis of a short-term energy measure.…”

Section: Introductionmentioning

confidence: 99%

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Implicit Wiener Filtering for Speech Enhancement In Non-Stationary Noise

Jaiswal

Romero

2021

2021 11th International Conference on Information Science and Technology (ICIST)

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Speech quality is degraded in the presence of background noise, which reduces the quality of experience (QoE) of the end-user and therefore motivates the usage of speech enhancement algorithms. A large number of approaches have been proposed in this context. However most of them have focused on the case where the noise is stationary, an assumption that seldom holds in practice. For instance, in mobile telephony, noise sources with a marked non-stationary spectral signature include vehicles, machines, and other speakers to name a few. On the other hand, the usage of frequency-domain information in existing algorithms for speech enhancement in non-stationary noise environments can be made more effective by leveraging the increased flexibility introduced by implicit Wiener filters, which allow the control of the spectral reconstruction of the speech signal through the adjustment of hyperparameters. To address these limitations, the present paper develops an algorithm that recursively estimates the noise power spectral density and reconstructs the target speech signal in the frequency domain by means of an implicit Wiener filter with judiciously selected hyperparameters. The recursive noise estimation approach relies on the past and the present power spectral values. To evaluate the performance of the speech enhancement algorithm, speech uttered by a male and a female speaker degraded by nonstationary noise produced e.g. by babbling, cars, street noise, trains, restaurants, and airport noise. To this end, the NOIZEUS corpus is used. Objective speech quality measures such as the log-likelihood ratio (LLR), the cepstral distance (CD), and the weighted spectral slope distance (WSS) are evaluated for the enhanced speech signals and compared to the conventional spectral subtraction method. Results demonstrate that the proposed algorithm provides consistent and improved enhancement performance with all tested noise types.

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“…that is, we have approximated the true power spectral density of s(n) by its spectral energy. From (7), the output of the Wiener filter in the frequency domain is given by…”

Section: The Implicit Wiener Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implicit Wiener Filtering for Speech Enhancement In Non-Stationary Noise

Jaiswal

Romero

2021

2021 11th International Conference on Information Science and Technology (ICIST)

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Single-channel speech enhancement using implicit Wiener filter for high-quality speech communication

Jaiswal

Yeduri

Cenkeramaddi

2022

Int J Speech Technol

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Speech enables easy human-to-human communication as well as human-to-machine interaction. However, the quality of speech degrades due to background noise in the environment, such as drone noise embedded in speech during search and rescue operations. Similarly, helicopter noise, airplane noise, and station noise reduce the quality of speech. Speech enhancement algorithms reduce background noise, resulting in a crystal clear and noise-free conversation. For many applications, it is also necessary to process these noisy speech signals at the edge node level. Thus, we propose implicit Wiener filter-based algorithm for speech enhancement using edge computing system. In the proposed algorithm, a first order recursive equation is used to estimate the noise. The performance of the proposed algorithm is evaluated for two speech utterances, one uttered by a male speaker and the other by a female speaker. Both utterances are degraded by different types of non-stationary noises such as exhibition, station, drone, helicopter, airplane, and white Gaussian stationary noise with different signal-to-noise ratios. Further, we compare the performance of the proposed speech enhancement algorithm with the conventional spectral subtraction algorithm. Performance evaluations using objective speech quality measures demonstrate that the proposed speech enhancement algorithm outperforms the spectral subtraction algorithm in estimating the clean speech from the noisy speech. Finally, we implement the proposed speech enhancement algorithm, in addition to the spectral subtraction algorithm, on the Raspberry Pi 4 Model B, which is a low power edge computing device.

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