Abstract
ABSTRACT In the field of sound recognition, the research and study of sound event detection are still active, with the vast majority of the papers focusing mostly on the domain of speech and music. This paper presents and discusses a framework for a study room event detection system. Feature extraction techniques are utilized and discussed to obtain the parametric type representation for the analysis of the sound for intelligent homes machine listening systems specifically for study room. The conduct of sound analysis within the category of the sounds the least accurate was the door knock, but the accuracy of 95.00%, currently in the field is acknowledged as good, making the parameters fit for detecting surrounding sounds. The performance of the CNN in detecting environmental sounds was analyzed using the parameters that were defined, with an overall accuracy of 96.8%. The result was promising for machine learning that detects sounds that can be applied as technology for an innovative learning environment.
Highlights
Several international interests and working groups have looked at the potential usefulness and advancement of the acoustic classification
Research suggests that developing signal processing methods to extract this information has enormous potential in several applications automatically, for example searching for multimedia based on its audio content, making context-aware mobile devices, robots, cars, and many others, and intelligent monitoring systems to recognize activities in their environments using acoustic information (Yamakawa et al, 2011)
Researchers have tried to apply in different fields and areas to see the results and measure the performance of the convolutional neural network (CNN)
Summary
Several international interests and working groups have looked at the potential usefulness and advancement of the acoustic classification. Research suggests that developing signal processing methods to extract this information has enormous potential in several applications automatically, for example searching for multimedia based on its audio content, making context-aware mobile devices, robots, cars, and many others, and intelligent monitoring systems to recognize activities in their environments using acoustic information (Yamakawa et al, 2011). Sounds carry a large amount of information about our everyday environment and physical events that take place in it, being able to detect which are the sound sources present in the signals would further increase the usefulness of any audio recording. The surrounding sounds are the most abundant, yet very few studies were conducted, this will be another contribution in the field of acoustic sound environment classification and innovative learning environment
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