Artificial Intelligent Systems and Machine Learning

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Classification of Audio Signals Using Feed Forward Neural Network to Vary the Number of Layers


Affiliations
1 Department of Electronics and Communication Engineering, Rayat Institute of Engineering and Information Technology, SBS Nagar, Punjab-144533, India
2 Department of Physics, Punjab University, Chandigarh-160014, India
     

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Classification of audio signals according to their content has been a major concern in recent years. There have been many studies on audio content analysis, using different features and different methods. It is a well-known fact that audio signals are baseband, one-dimensional signals. General audio consists of a wide range of sound phenomena such as music, sound effects, environmental sounds, speech and nonspeech signals. In this paper we classified the audio systems using feedforward neural network to measure the suitability for accuracy in classification and time taken to classify. Here we have investigated and analyzed this system to optimize the neural networks as to what layers our system is most suitable to classify audio wave files. Here accuracy of above 99% is reported.
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  • Classification of Audio Signals Using Feed Forward Neural Network to Vary the Number of Layers

Abstract Views: 292  |  PDF Views: 5

Authors

Onkar Singh
Department of Electronics and Communication Engineering, Rayat Institute of Engineering and Information Technology, SBS Nagar, Punjab-144533, India
Neeru Singla
Department of Electronics and Communication Engineering, Rayat Institute of Engineering and Information Technology, SBS Nagar, Punjab-144533, India
Manish Dev Sharma
Department of Physics, Punjab University, Chandigarh-160014, India

Abstract


Classification of audio signals according to their content has been a major concern in recent years. There have been many studies on audio content analysis, using different features and different methods. It is a well-known fact that audio signals are baseband, one-dimensional signals. General audio consists of a wide range of sound phenomena such as music, sound effects, environmental sounds, speech and nonspeech signals. In this paper we classified the audio systems using feedforward neural network to measure the suitability for accuracy in classification and time taken to classify. Here we have investigated and analyzed this system to optimize the neural networks as to what layers our system is most suitable to classify audio wave files. Here accuracy of above 99% is reported.