On the Significance of an Intensive Survey in Relation to Community Response to Noise

Minoura, Kazuya; Hiramatsu, Ken

doi:10.1006/jsvi.1997.1012

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

1997

2023

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Community Noise and Vibration Sources

2020

Engineering Acoustics

View full text Add to dashboard Cite

Community Noise and Vibration Sources

2020

Engineering Acoustics

View full text Add to dashboard Cite

Time frequency domain deep CNN for automatic background classification in speech signals

Yakkati,

Yeduri,

Tripathy

et al. 2023

Int J Speech Technol

View full text Add to dashboard Cite

Many application areas, such as background identification, predictive maintenance in industrial applications, smart home applications, assisting deaf people with their daily activities and indexing and retrieval of content-based multimedia, etc., use automatic background classification using speech signals. It is challenging to predict the background environment accurately from speech signal information. Thus, a novel synchrosqueezed wavelet transform (SWT)-based deep learning (DL) approach is proposed in this paper for automatically classifying background information embedded in speech signals. Here, SWT is incorporated to obtain the time-frequency plot from the speech signals. These time-frequency signals are then fed to a deep convolutional neural network (DCNN) to classify background information embedded in speech signals. The proposed DCNN model consists of three convolution layers, one batch-normalization layer, three max-pooling layers, one dropout layer, and one fully connected layer. The proposed method is tested using various background signals embedded in speech signals, such as airport, airplane, drone, street, babble, car, helicopter, exhibition, station, restaurant, and train sounds. According to the results, the proposed SWT-based DCNN approach has an overall classification accuracy of 97.96 (± 0.53)% to classify background information embedded in speech signals. Finally, the performance of the proposed approach is compared to the existing methods.

show abstract

Environmental acoustics update.

Maekawa

1997

J. Acoust. Soc. Jpn. (E), J Acoust Soc Jpn E

View full text Add to dashboard Cite

On the Significance of an Intensive Survey in Relation to Community Response to Noise

Cited by 3 publications

References 0 publications

Community Noise and Vibration Sources

Community Noise and Vibration Sources

Time frequency domain deep CNN for automatic background classification in speech signals

Environmental acoustics update.

Contact Info

Product

Resources

About