<i>Identification of Background Noise by Mel-scale Frequency Cepstral Coefficients (MFCC) and Vector Quantisation (VQ) for Piglets Sound Detection</i>

2018 ◽  
Author(s):  
Jheng-Hong Hsieh ◽  
Ching-Lu Hsieh
Keyword(s):  
Background Noise ◽  
Vector Quantisation ◽  
Sound Detection ◽  
Cepstral Coefficients

Robust Feature Vector Set Using Higher Order Autocorrelation Coefficients

2012 ◽  
pp. 126-134
Author(s):  
Poonam Bansal ◽  
Amita Dev ◽  
Shail Jain
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Background Noise ◽  
Extraction Method ◽  
Recognition Performance ◽  
Spectral Estimation ◽  
Higher Order ◽  
Feature Extraction Method ◽  
Power Spectral ◽  
Cepstral Coefficients

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower orders, while the higher-order autocorrelation coefficients are least affected, this method discards the lower order autocorrelation coefficients and uses only the higher-order autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-order autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further by the Mel filter bank; a log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the Differentiated Relative Higher Order Autocorrelation Coefficient Sequence Spectrum (DRHOASS). The authors evaluate the speech recognition performance of the DRHOASS features and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech.

scholarly journals The Importance of Sounds to Fishes

2020 ◽  
pp. 1-14
Keyword(s):  
Fish Species ◽  
Background Noise ◽  
Anthropogenic Sources ◽  
Underwater Noise ◽  
Closely Related Species ◽  
Low Frequencies ◽  
Natural Sources ◽  
Sound Detection ◽  
Reproductive Activities ◽  
The Impact

Fishes can detect underwater sounds and use them to obtain key information about the environment around them. Sounds travel rapidly over great distances in water and can provide detailed information on the presence of prey, predators, and related fishes, while the overall acoustic scene provides the fishes with key information about their environment. Although they do not have the external ears that many vertebrates have, all fish species have effective internal ears. Many fish species engage in making sounds themselves. Their calls are often produced when they are approached by other fish species, and they can be used to startle and deflect their opponents. Sounds are also produced during reproductive activities. There are often differences in the sounds made by fish species, even between closely related species. The sounds of individuals may also differ, and this may play a role in sexual selection, as males compete with one another and aim to attract females that are looking for the best males to mate with. The sounds that fishes can hear are confined to low frequencies, although this is species- dependent. It is evident that fishes can distinguish between sounds that differ in their amplitude and frequency, and also discriminate between sounds that have different temporal characteristics. They can also distinguish between sounds that arrive from different directions and distances, in some cases enabling them to locate the sources of sound. Detecting sounds may enable fishes to navigate and move to particular habitats, search for prey, move away from predators, and communicate during spawning. However, a particular problem in sound detection is the masking of those sounds that interest the fishes by high and variable levels of background noise. Although some of the background noise is generated by natural sources, including the precipitation of rain and snow, and wind and waves, many underwater sounds now come from anthropogenic sources. Some of these human-made sounds can kill or injure fishes, impair their hearing, and alter their behaviour. Interference with the detection of sounds can have especially adverse effects upon the lives of fishes. There is a need for more work on the impact of human- made underwater noise upon the fitness of fishes, and the strength of fish populations.

Robust Feature Vector Set Using Higher Order Autocorrelation Coefficients

2010 ◽  
Vol 4 (4) ◽  
pp. 37-46
Author(s):  
Poonam Bansal ◽  
Amita Dev ◽  
Shail Jain
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Background Noise ◽  
Recognition Performance ◽  
Spectral Estimation ◽  
Higher Order ◽  
Feature Extraction Method ◽  
Power Spectral ◽  
Cepstral Coefficients ◽  
Better Than

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower orders, while the higher-order autocorrelation coefficients are least affected, this method discards the lower order autocorrelation coefficients and uses only the higher-order autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-order autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further by the Mel filter bank; a log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the Differentiated Relative Higher Order Autocorrelation Coefficient Sequence Spectrum (DRHOASS). The authors evaluate the speech recognition performance of the DRHOASS features and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

Use of a Mild-Gain Hearing Aid by Middle-Age Normal-Hearing Adults Who Do and Do Not Self-Report Trouble Hearing in Background Noise

2020 ◽  
Vol 29 (3) ◽  
pp. 419-428
Author(s):  
Jasleen Singh ◽  
Karen A. Doherty
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Middle Age ◽  
Hearing Aid ◽  
Normal Hearing ◽  
Self Report ◽  
Hearing Aid Use ◽  
Hearing Handicap ◽  
Before And After ◽  
Hearing Problems

Purpose The aim of the study was to assess how the use of a mild-gain hearing aid can affect hearing handicap, motivation, and attitudes toward hearing aids for middle-age, normal-hearing adults who do and do not self-report trouble hearing in background noise. Method A total of 20 participants (45–60 years of age) with clinically normal-hearing thresholds (< 25 dB HL) were enrolled in this study. Ten self-reported difficulty hearing in background noise, and 10 did not self-report difficulty hearing in background noise. All participants were fit with mild-gain hearing aids, bilaterally, and were asked to wear them for 2 weeks. Hearing handicap, attitudes toward hearing aids and hearing loss, and motivation to address hearing problems were evaluated before and after participants wore the hearing aids. Participants were also asked if they would consider purchasing a hearing aid before and after 2 weeks of hearing aid use. Results After wearing the hearing aids for 2 weeks, hearing handicap scores decreased for the participants who self-reported difficulty hearing in background noise. No changes in hearing handicap scores were observed for the participants who did not self-report trouble hearing in background noise. The participants who self-reported difficulty hearing in background noise also reported greater personal distress from their hearing problems, were more motivated to address their hearing problems, and had higher levels of hearing handicap compared to the participants who did not self-report trouble hearing in background noise. Only 20% (2/10) of the participants who self-reported trouble hearing in background noise reported that they would consider purchasing a hearing aid after 2 weeks of hearing aid use. Conclusions The use of mild-gain hearing aids has the potential to reduce hearing handicap for normal-hearing, middle-age adults who self-report difficulty hearing in background noise. However, this may not be the most appropriate treatment option for their current hearing problems given that only 20% of these participants would consider purchasing a hearing aid after wearing hearing aids for 2 weeks.

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

Hearing Aid Design and Evaluation for a Patient with a Severe Discrimination Loss for Speech

1967 ◽  
Vol 10 (2) ◽  
pp. 367-372 ◽  
Author(s):  
James D. Miller ◽  
Arthur F. Niemoeller
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Hearing Aid ◽  
Accurate Estimate ◽  
Single Patient

Results of intelligibility tests on a single patient with a severe discrimination loss for speech are reported. The patient was tested with four different hearing aids and with no aid, and the effects of opportunity for lipreading, background noise, and reverberation were evaluated. The tests appear to allow an accurate estimate of the amount of help to be expected in various situations and show that an aid with good fidelity is clearly superior to the others tested. The destructive effects of background noise and reverberation are demonstrated separately and in combination.

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