A Spectral Subtraction Based Algorithm for Real-time Noise Cancellation with Application to Gunshot Acoustics

Ramos, Antônio L. L.; Holm, Sverre; Gudvangen, S.; Otterlei, Ragnvald

doi:10.2478/eletel-2013-0011

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…The spectral subtraction method is a simple and effective method of noise reduction [5] . In this method, an average signal spectrum and average noise spectrum are estimated in parts of the recording and subtracted from each other, so that average signal-to-noise ratio (SNR) is improved.…”

Section: Spectral Subtraction Methodsmentioning

confidence: 99%

Research on Speech Enhancement Algorithm Based on EMD in Noisy Environments

2016

ISSPJ

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This paper presents a new technique for speech enhancement in a noisy environment based on the empirical mode decomposition (EMD) algorithm and spectral subtraction. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then spectral subtraction of the IMF components with noise can be used to eliminate the effect of the noise for speech enhancement. Experimental results demonstrate the advantage of using the proposed simultaneous detection and estimation approach with the proposed algorithm, which facilitate suppression of transient noise with a controlled level of speech distortion.

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Section: Spectral Subtraction Methodsmentioning

confidence: 99%

Research on Speech Enhancement Algorithm Based on EMD in Noisy Environments

2016

ISSPJ

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“…Antonio et al [40] proposed an improved algorithm based on the SS for real-time noise cancellation and applied the algorithm to the gunshot acoustical signals. A pre-processing approach based on spectral suppression algorithm is applied instead of post-filtering, which requires a priori information concerning the direction of arrival of desired signals.…”

Section: Analysis Synthesis Snr Estimationmentioning

confidence: 99%

On Improvement of Speech Intelligibility and Quality: A Survey of Unsupervised Single Channel Speech Enhancement Algorithms

Verdú¹,

Saleem²,

Khattak³

2020

IJIMAI

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Many forms of human communication exist; for instance, text and nonverbal based. Speech is, however, the most powerful and dexterous form for the humans. Speech signals enable humans to communicate and this usefulness of the speech signals has led to a variety of speech processing applications. Successful use of these applications is, however, significantly aggravated in presence of the background noise distortions. These noise signals overlap and mask the target speech signals. To deal with these overlapping background noise distortions, a speech enhancement algorithm at front end is crucial in order to make noisy speech intelligible and pleasant. Speech enhancement has become a very important research and engineering problem for the last couple of decades. In this paper, we present an all-inclusive survey on unsupervised single-channel speech enhancement (U-SCSE) algorithms. A taxonomy based review of the U-SCSE algorithms is presented and the associated studies regarding improving the intelligibility and quality are outlined. The studies on the speech enhancement algorithms in unsupervised perspective are presented. Objective experiments have been performed to evaluate the potential of the U-SCSE algorithms in terms of improving the speech intelligibility and quality. It is found that unsupervised speech enhancement improves the speech quality but the speech intelligibility improvement is deprived. To finish, several research problems are identified that require further research.

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“…As we are dealing with highly noisy signals, signal enhancement techniques such as spectral subtraction [13, 14] or median filtering (MF) [28, 29] could be applied to reduce the additive noise that contaminates the gunshot signals prior to employing an efficient DOA estimation scheme such as the ES‐SCFL algorithm (without altering the thresholds). Particularly, the use of MF has proved effective in gunshot signal denoising for DOA estimation [30].…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…To overcome the problem of spurious DOA estimation due to strong noise, in [10], a data selection algorithm, known as iterative least squares (ILSs) [11, 12], was applied after spectral subtraction [13, 14], which improves the accuracy of DOA estimation for gunshot signals with SNR between −3 and 2 dB. In [15], we developed a DOA estimation algorithm based on exhaustive search (ES) for an improved result with a reduced number of pairs of microphones, applicable to gunshot signals with SNR below these values.…”

Section: Introductionmentioning

confidence: 99%