Using wavelet transformation for acoustic signal processing in heavy vehicle detection and classification

Krishnamurthi, Rajalakshmi; Gopinathan, Dhanalekshmi; Kumar, Adarsh

doi:10.1016/b978-0-323-90592-3.00011-2

Cited by 3 publications

References 7 publications

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Defining Mechanistic Pathways for Anthropogenic Noise Impact on Avian Species

Engel,

Young,

Davies

et al. 2024

Curr Pollution Rep

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Purpose of Review This review collates and analyses data on noise exposure of birds in relation to avian hearing system performance. It provides new insights into the mechanistic pathways of anthropogenic noise impact on avian species. Recent Findings Noise impacts both humans and wildlife. Birds are of conservation concern, given the recent reports of major global declines in bird populations and that one in eight bird species is threatened with extinction. Studies of noise impacts on birds have been, and continue to be, published. Whilst many of these studies report associations between noise and a response in birds, relatively few provide clear demonstration of the mechanisms of impact. Summary Anatomical and physiological datasets were compiled for species representing nineteen avian orders. Information on noise sources, propagation path and habitat selection was also collated. Bird order was not a good predictor of bird hearing frequencies, but body dimensions were. In general, smaller birds were found to have higher peak hearing frequencies than larger birds. Cranium height was the strongest predictor of peak hearing frequency for birds. These findings provide mechanistic context to noise impacts on birds and a potential basis for predicting responses of avian species to different noise environments.

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Defining Mechanistic Pathways for Anthropogenic Noise Impact on Avian Species

Engel,

Young,

Davies

et al. 2024

Curr Pollution Rep

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Identification of habitual smokers through speech signal

Vijean,

Santiagoo,

Ahmad

et al. 2024

AIP Conference Proceedings

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Assessment of Pepper Robot’s Speech Recognition System through the Lens of Machine Learning

Pande,

Mishra

2024

Biomimetics

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Speech comprehension can be challenging due to multiple factors, causing inconvenience for both the speaker and the listener. In such situations, using a humanoid robot, Pepper, can be beneficial as it can display the corresponding text on its screen. However, prior to that, it is essential to carefully assess the accuracy of the audio recordings captured by Pepper. Therefore, in this study, an experiment is conducted with eight participants with the primary objective of examining Pepper’s speech recognition system with the help of audio features such as Mel-Frequency Cepstral Coefficients, spectral centroid, spectral flatness, the Zero-Crossing Rate, pitch, and energy. Furthermore, the K-means algorithm was employed to create clusters based on these features with the aim of selecting the most suitable cluster with the help of the speech-to-text conversion tool Whisper. The selection of the best cluster is accomplished by finding the maximum accuracy data points lying in a cluster. A criterion of discarding data points with values of WER above 0.3 is imposed to achieve this. The findings of this study suggest that a distance of up to one meter from the humanoid robot Pepper is suitable for capturing the best speech recordings. In contrast, age and gender do not influence the accuracy of recorded speech. The proposed system will provide a significant strength in settings where subtitles are required to improve the comprehension of spoken statements.

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Using wavelet transformation for acoustic signal processing in heavy vehicle detection and classification

Cited by 3 publications

References 7 publications

Defining Mechanistic Pathways for Anthropogenic Noise Impact on Avian Species

Defining Mechanistic Pathways for Anthropogenic Noise Impact on Avian Species

Identification of habitual smokers through speech signal

Assessment of Pepper Robot’s Speech Recognition System through the Lens of Machine Learning

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