Open AccessUnsupervised Neural Network approach for the Identification of Anomaly in Speech Signal from Spectrogram Images
Author(s) -
M. Joshwin Darington,
V. Sathiesh Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1911/1/012025
Subject(s) - spectrogram , pattern recognition (psychology) , signal (programming language) , centroid , artificial neural network , computer science , artificial intelligence , speech recognition , feature (linguistics) , mel frequency cepstrum , anomaly detection , anomaly (physics) , cepstrum , feature extraction , identification (biology) , cluster analysis , physics , philosophy , linguistics , botany , biology , programming language , condensed matter physics
In this paper, anomaly identification in speech signal is carried out using an unsupervised Neural Network approach. Input audio signal is divided into small segments of information. Initial segment of the samples are used in the training process. The later portion of the sample segments are used in the test process. The features (Spectral Roll-off, Spectral Centroid, Mel Frequency Cepstral Coefficient (MFFC), Pitch (PH) and Energy Density (ED)) are extracted from the input data. The extracted 1D features are converted into spectrogram images. Then, the images are fed as an input to Neural Network for the prediction of feature values. If three or more feature value does not exceed the threshold value of 75% then the input signal is considered to be free from anomaly. The proposed model results in an accuracy of 97.50% in the detection of anomaly in input speech signal.