Location based applications for mobile augmented reality
AUIC '03 Proceedings of the Fourth Australasian user interface conference on User interfaces 2003 - Volume 18
Environmental sound recognition with time-frequency audio features
IEEE Transactions on Audio, Speech, and Language Processing
Environmental sound recognition for robot audition using matching-pursuit
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
A study on dynamic state information (DSI) around users for safe urban life
Computers & Mathematics with Applications
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Audio-based context recognition
IEEE Transactions on Audio, Speech, and Language Processing
Multistability of α-divergence based NMF algorithms
Computers & Mathematics with Applications
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Various environmental sounds exist around us in our daily life. Recently, environmental sound recognition has drawn great attention for understanding our environment. However, because environmental sounds derive from multiple sound sources, it is difficult to recognize them accurately. If we were able to separate sound sources before sound recognition as a pre-process, then recognition would be easier and more accurate. We assume that monaural microphones are widely installed in mobile devices used as recording devices. This paper therefore presents a proposal for monaural sound source separation of environmental sounds. Two-phase clustering using non-negative matrix factorization (NMF) is proposed to separate monaural sound sources. In this proposal, the time-variant gain feature is used as an attribute of an environmental sound for more efficient sound separation.