Abstract
This paper presents a model-free and training-free two-phase method for audio segmentation that separates monophonic heterogeneous audio files into acoustically homogeneous regions where each region contains a single sound. A rough segmentation separates audio input into audio clips based on silence detection in the time domain. Then a self-similarity matrix, based on selected audio features in the frequency domain to discover the level of similarity between frames in the audio clip, is calculated. Subsequently an edge detection method is used to find regions in the similarity image that determine plausible sounds in the audio clip. The results of the two phases are combined to form the final boundaries for the input audio. This two-phase method is evaluated using established methods and a standard non-musical database. The method reported here offers more accurate segmentation results than existing methods for audio segmentation. We propose that this approach could be adapted as an efficient preprocessing stage in other audio processing systems such as audio retrieval, classification, music analysis and summarization.
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.
