02768nas a2200253   4500008004100000022001300041245010800054210006900162260001600231300001400247490000700261520186000268653004202128653005702170653005502227653002202282653003402304653002402338653002302362100002002385700002202405700002102427856006602448       2012                        eng d  a0167639300aSpeech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator0 aSpeech enhancement using a minimum meansquare error shorttime sp  cJan-02-2012  a282 - 3050 v543 a<p>In this paper we investigate the enhancement of speech by applying MMSE short-time spectral magnitude estimation in the modu- lation domain. For this purpose, the traditional analysis-modification-synthesis framework is extended to include modulation domain processing. We compensate the noisy modulation spectrum for additive noise distortion by applying the MMSE short-time spectral mag- nitude estimation algorithm in the modulation domain. A number of subjective experiments were conducted. Initially, we determine the parameter values that maximise the subjective quality of stimuli enhanced using the MMSE modulation magnitude estimator. Next, we compare the quality of stimuli processed by the MMSE modulation magnitude estimator to those processed using the MMSE acoustic magnitude estimator and the modulation spectral subtraction method, and show that good improvement in speech quality is achieved through use of the proposed approach. Then we evaluate the effect of including speech presence uncertainty and log-domain processing on the quality of enhanced speech, and find that this method works better with speech uncertainty. Finally we compare the quality of speech enhanced using the MMSE modulation magnitude estimator (when used with speech presence uncertainty) with that enhanced using different acoustic domain MMSE magnitude estimator formulations, and those enhanced using different modulation domain based enhancement algorithms. Results of these tests show that the MMSE modulation magnitude estimator improves the quality of processed stimuli, without introducing musical noise or spectral smearing distortion. The proposed method is shown to have better noise suppres- sion than MMSE acoustic magnitude estimation, and improved speech quality compared to other modulation domain based enhance- ment methods considered.</p>
10aAnalysis-modification-synthesis (AMS)10aMMSE short-time modulation magnitude estimator (MME)10aMMSE short-time spectral magnitude estimator (AME)10aModulation domain10aModulation magnitude spectrum10aModulation spectrum10aSpeech enhancement1 aPaliwal, Kuldip1 aSchwerin, Belinda1 aWójcicki, Kamil  uhttp://linkinghub.elsevier.com/retrieve/pii/S016763931100135X02366nas a2200193   4500008004100000245008100041210006900122260001600191300001600207490000600223520178500229653001302014653001702027653001802044100002102062700001802083700002202101856004902123       2018                        eng d00aAutomatic and Efficient Denoising of Bioacoustics Recordings Using MMSE STSA0 aAutomatic and Efficient Denoising of Bioacoustics Recordings Usi  cJan-01-2018  a5010 - 50220 v63 a<p>Automatic recording and analysis of bird calls is becoming an important way to understand changes in bird populations and assess environmental health. An issue currently proving problematic with the automatic analysis of bird recordings is interference from noise that can mask vocalizations of interest. As such, noise reduction can greatly increase the accuracy of automatic analyses and reduce processing work for subsequent steps in bioacoustics analyses. However, only limited work has been done in the context of bird recordings. Most semiautomatic methods either manually apply sound enhancement methods available in audio processing systems such as SoX and Audacity or apply preliminary filters such as low- and high-pass filters. These methods are insufficient both in terms of how generically they can be applied and their integration with automatic systems that need to process large amounts of data. Some other work applied more sophisticated denoising methods or combinations of different methods such as minimum mean square error short-time spectral amplitude estimator (MMSE STSA) and spectral subtraction for other species such as anurans. However, their effectiveness is not tested on bird recordings. In this paper, we analyze the applicability of the MMSE STSA algorithm to remove noise from environmental recordings containing bird sounds, particularly focusing on its quality and processing time. The experimental evaluation using real data clearly shows that MMSE STSA can reduce noise with similar effectiveness [using objective metrics such as predicted signal quality (SIG)] to a previously recommended wavelet-transform-based denoising technique while executing between approximately 5&ndash;300 times faster depending on the audio files tested.</p>
10abig data10abioacoustics10aNoise removal1 aBrown, Alexander1 aGarg, Saurabh1 aMontgomery, James  uhttp://ieeexplore.ieee.org/document/8194836/