Spectral Noise Gate Technique Applied to Birdsong Preprocessing on Embedded Unit

Kiapuchinski, Davi Miara; Lima, Carlos R. Erig; Kaestner, Celso A. A.

doi:10.1109/ism.2012.12

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…Determining what constitutes noise in recordings is non-trivial and impacts what type of noise reduction algorithm can and should be used. In a systematic review of noise reduction methods in bio-acoustics, Xie et al ( 2020 ) outline six classes of noise reduction algorithms used for bio-acoustics: (1) Optimal FIR filter (e.g., Kim et al, 2000 ), (2) spectral subtraction (e.g., Boll, 1979 ; Kiapuchinski et al, 2012 ; Sainburg et al, 2020b ), (3) minimum-mean square error short-time spectral amplitude estimator (MMSE-STSA; e.g., Ephraim and Malah, 1984 ; Alonso et al, 2017 ; Brown et al, 2017 ) (4) wavelet based denoising (e.g., Ren et al, 2008 ; Priyadarshani et al, 2016 ) (5) image processing based noise reduction, and (6) deep learning based noised reduction. These noise reduction algorithms can be broadly divided into two categories: stationary and non-stationary noise reduction ( Figure 1A ).…”

Section: Signal Processing and Denoisingmentioning

confidence: 99%

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Sainburg

Gentner

2021

Front. Behav. Neurosci.

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Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

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Section: Signal Processing and Denoisingmentioning

confidence: 99%

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Sainburg

Gentner

2021

Front. Behav. Neurosci.

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“…The name Audacity® is a registered trademark of Dominic Mazzoni) for background noise reduction. The noise reduction algorithm implemented in Audacity is the spectral noise gate algorithm [12,13]. The algorithm detects the noise profile of the recording and then uses this profile to filter and smooth the audio signal.…”

Section: Audio Signal Analysis Of Ictal Vocalizationmentioning

confidence: 99%

Quantitative and qualitative analysis of ictal vocalization in focal epilepsy syndromes

Härtl¹,

Knoche²,

Choupina

et al. 2018

Seizure

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Although ictal vocalization is similarly common in different focal epilepsies, it shows localizing significance when taken into account the co-occurring seizure semiology. It especially increases the localizing value of automatisms, predicting a temporal seizure onset with a sensitivity of 92% and specificity of 70%. Quantitative parameters of the intensity dynamic objectively distinguished frontal lobe seizures, establishing an observer independent tool for semiological seizure evaluation.

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“…A de-noise filter [16] is first employed to remove noise during the signal analysis. Then, Fourier analysis and spectral noise gating [17] are applied to retrieve the de-noise parameters and "purify" the data in the noisy environment.…”

Section: A Signal Preprocessingmentioning

confidence: 99%

An Intelligent Nocturnal Animal Vocalization Recognition System

Chen¹,

Huang²,

Chen³

et al. 2015

IJCCE

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An intelligent nocturnal animal identification system is proposed in this work to assist in the recognition of different kinds of nocturnal animals. Firstly, each sound sample underwent the steps of the noise reduction and the syllable segmentation. Next, the segmentation was converted into Mel Frequencies Cepstral Coefficients (MFCCs) as the main identification feature of the proposed work. To increase the recognition accuracy and lower the computation overhead, the system can guide a user to input environmental information as the parameters of the decision tree classification module. It then proceeds with the clustering, and the results are classified with a composite classifier. A series of simulations were conducted to demonstrate the feasibility of the proposed algorithm. Drawing from 18 species, 408 sound samples, and 4477 feature segmentations were utilized to verify the accuracy rate of our work. Our simulations showed that the proposed approach can effectively increase the recognition rate and decrease the training time. In particular, the collected sound sample of the Apus affinis is only one that results in a lower accuracy rate. We expect to expand the database of sound samples, and thus we expect to be able to dramatically improve on the above-mentioned problem, thereby increasing the accuracy rate for each species.

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Spectral Noise Gate Technique Applied to Birdsong Preprocessing on Embedded Unit

Cited by 14 publications

References 8 publications

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Quantitative and qualitative analysis of ictal vocalization in focal epilepsy syndromes

An Intelligent Nocturnal Animal Vocalization Recognition System

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