Abstract
With a wide variety of applications for audio classification, multimedia content analysis has become crucial in the educational field as online lectures are being increasingly practiced. The breaks pause and non-stationeries present in the audio signals make segmentation and classification of the signal quite a challenging and demanding process. This motivates building a package that aims to analyze the multimedia content for achieving novel means to classify audio into different categories specific to an application. Although work on audio classification exists, a majority of it attempts to classify the audio into predefined categories like silence and music. Our package is an attempt to obtain maximum accuracy in classifying the data into whatever categories are specified by the input data. Various simple features including, the average crossing rate, spectral features, energy function are extricated. This paper presents an approach to achieve efficient segmentation, feature extraction, and classification of audio signals.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
