Paper Info
Title
Recognition of harmonic sounds in polyphonic audio using a missing feature approach
Abstract
A method based on local spectral features and missing feature techniques is proposed for the recognition of harmonic sounds in mixture signals. A mask estimation algorithm is proposed for identifying spectral regions that contain reliable information for each sound source and then bounded marginalization is employed to treat the feature vector elements that are determined as unreliable. The proposed method is tested on musical instrument sounds due to the extensive availability of data but it can be applied on other sounds (i.e. animal sounds, environmental sounds), whenever these are harmonic. In simulations the proposed method clearly outperformed a baseline method for mixture signals.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639356
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
audio signal processing,animal sounds,baseline method,bounded marginalization,environmental sounds,feature vector elements,harmonic sounds recognition,mask estimation algorithm,missing feature approach,missing feature techniques,mixture signals,musical instrument sounds,polyphonic audio,spectral regions
Environmental sounds,Feature vector,Pattern recognition,Computer science,Harmonic,Musical instrument,Speech recognition,Artificial intelligence,Polyphony,Audio signal processing,Bounded function
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.60
References 
Authors
13
3
Name
Order
Citations
PageRank
Dimitrios Giannoulis130114.36
Anssi Klapuri285870.02
M. D. Plumbley31915202.38