KEYWORDS: Speech recognition, Data modeling, Performance modeling, Evolutionary algorithms, Acoustics, Detection and tracking algorithms, Signal processing, Process modeling, Optical filters
In this paper, we focus on the application of the LightGBM model for audio sound classification. Though convolutional neural networks (CNN) generally have superior performance, LightGBM model possess certain notable advantages, such as low computational costs, feasibility of parallel implementations, and comparable accuracies over many datasets. In order to improve the generalization ability of the model, data augmentation operations are performed on the audio clips including pitch shifting, time stretching, compressing the dynamic range and adding white noise. The accuracy of speech recognition heavily depends on the reliability of the representative features extracted from the audio signal. The audio signal is originally a one-dimensional time series signal, which is difficult to visualize the frequency change. Hence it is necessary to extract the discernible components in the audio signal. To improve the representative capacity of our proposed model, we use the Mel spectrum and MFCC (Mel-Frequency Cepstral Coefficients) to select features as twodimensional input to accurately characterize the internal information of the signal. The techniques mentioned in this paper are mainly trained on Google Speech Commands dataset. The experimental results show that the method, which is an optimized LightGBM model based on the Mel spectrum, can achieve high word classification accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.