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Separation of Overlapping Sound using Nonnegative Matrix Factorization
Ranny R.a,b, Lestari D.P.a, Rajab T.L.E.a, Suwardi I.S.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, Indonesia
b School of Computer Science, Bina Nusantara University, Bandung Campus, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.One of the most common problems in sound recognition is the overlapping sound. This phenomena requires sound separation beforehand in order to be recognized. Most studies related to sound separation used artificial data in their research, i.e. using experiment sound data from a controlled environment which is augmented with one or more sound types, and achieve good results. However, when it is implemented in the real condition, it’s performance has dropped dramatically. Thus, in this research we use overlapping data recorded in real environments. The purpose of this research is to separate the speech and non-speech, and noise by using the Non-negative Matrix Factorization (NMF). Our experimental results show that the NMF works well when separating sound and non-sound, and has helped the performance of sound recognition.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Artificial data,Controlled environment,Nonnegative matrix factorization,Overlapping data,Real environments,Sound data,Sound recognition,Sound separation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]NMF,sound recognition,sound separation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISRITI48646.2019.9034580[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]