A noise-estimation algorithm for highly non-stationary environments

Rangachari, Sundarrajan; Loizou, Philipos C.

doi:10.1016/j.specom.2005.08.005

Cited by 338 publications

(201 citation statements)

References 20 publications

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“…No adjustments were made for the algorithms (e.g., [11]) originally designed for a sampling rate of 16 kHz. To assess the merit of noiseestimation algorithms, two speech-enhancement algorithms (denoted in Table 3 with the suffix -ne) were also implemented with a noise-estimation algorithm [23]. That is, a noise-estimation algorithm was used in the speech enhancement algorithms indicated in Table 3 with -ne, to estimate and update the noise spectrum.…”

Section: Algorithms Evaluatedmentioning

confidence: 99%

Subjective comparison and evaluation of speech enhancement algorithms

Loizou

2007

Speech Communication

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632

267

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Making meaningful comparisons between the performance of the various speech enhancement algorithms proposed over the years, has been elusive due to lack of a common speech database, differences in the types of noise used and differences in the testing methodology. To facilitate such comparisons, we report on the development of a noisy speech corpus suitable for evaluation of speech enhancement algorithms. This corpus is subsequently used for the subjective evaluation of 13 speech enhancement methods encompassing four classes of algorithms: spectral subtractive, subspace, statistical-model based and Wiener-type algorithms. The subjective evaluation was performed by Dynastat, Inc. using the ITU-T P.835 methodology designed to evaluate the speech quality along three dimensions: signal distortion, noise distortion and overall quality. This paper reports the results of the subjective tests.

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Section: Algorithms Evaluatedmentioning

confidence: 99%

Subjective comparison and evaluation of speech enhancement algorithms

Loizou

2007

Speech Communication

Self Cite

632

267

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“…The spectrograms were produced based on the proposed developed models and related with [17]. The soil density estimation needs to be determined first with minimal data input such as soil type, return loss and reflection coefficient values within spectrum of frequencies.…”

Section: Soil Density Experiments Using Radio-wave Reflection Methodsmentioning

confidence: 99%

A Novel of Surface Reflection Method Using Radio-Wave for Soil Density Estimation

Roslee¹,

Shahdan²,

Subari³

2014

PIER M

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Abstract-Soil density is one of the important parameters to be investigated in civil, geological and agricultural works. Unfortunately, the challenging issue is found on a suitable model in determining accurately the soil density. In this article, a new soil density model based on radio-wave surface reflection method is presented. The development of the model is based on result analysis collected from the experiment. Then, comparisons with related theoretical models, Hallikainen and Topp, are performed. The experiment is performed by using a vector network analyzer (VNA) that generates radar signal and recording return loss (S 11 ) from a horn antenna. In the analysis, two new proposed soil density models have shown good agreement for soil density from 1.1 g/cm 3 to 1.7 g/cm 3 for sand and silty sand samples. This is verified when the model able to predict real samples as the one used in the experiment and result shows a very small relative error within 0.05% and 6.87%. Additionally, spectrograms in real time are produced in this study in order to observe more on the soil density. By using the proposed developed models, soil density estimation can be easily determined with minimal data input such as soil type, return loss and reflection coefficient by using regular radio-wave devices.

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“…We apply a signal enhancement algorithm developed by Rangachari & Loizou [24] because it has proven to be both efficient and effective. A key aspect of this algorithm is that it can perform noise-estimation in highly non-stationary noise environments such as what might be encountered at a construction job site.…”

Section: Denoisingmentioning

confidence: 99%

“…An estimate of the noise is continuously updated in every frame using time-frequency smoothing factors computed based on signal-presence probability in each frequency bin of the noisy recording spectrum. More details about this algorithm can be found in [24].…”

Section: Denoisingmentioning

confidence: 99%

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

Cheng¹,

Rashidi²,

Davenport³

et al. 2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Action recognition and tracking of construction heavy equipment is the first step for benchmarking and analyzing the performance of individual machines and evaluating the productivity of a jobsite as a whole. Aside from direct observations, the current approaches for automatically recognizing and tracking various actions of construction heavy equipment includes: 1) using active sensors such as RFID tags, GPS and accelerometers or 2) computer vision-based activity analysis (processing images or videos).In this paper, we present a novel audio-based approach for activity recognition of construction heavy equipment. Construction machines often produce distinct sound patterns while performing certain activities and it is possible to extract useful information by recording and processing those audio files at construction jobsites. The proposed audiobased framework begins with recording generated sound patterns of construction equipment using commercially available audio recorders. The recorded signal is then fed into a signal enhancement algorithm to reduce background noise commonly found at construction jobsites. The modified audio signal is then converted into a time-frequency representation using the Short-Time Fourier Transform (STFT). A Support Vector Machine (SVM) is then trained to differentiate between the acoustic patterns of the various activities of each machine. The processed audio signal is then finally divided and classified into various activities using a window filtering approach and by setting proper thresholds. We implemented the presented audio-based system at several jobsites as case studies and the results illustrate the efficiency of the system in automatically recognizing various actions of construction heavy equipment.

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A noise-estimation algorithm for highly non-stationary environments

Cited by 338 publications

References 20 publications

Subjective comparison and evaluation of speech enhancement algorithms

Subjective comparison and evaluation of speech enhancement algorithms

A Novel of Surface Reflection Method Using Radio-Wave for Soil Density Estimation

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

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