Multi-Talker Speech Recognition Based on Blind Source Separation with ad hoc Microphone Array Using Smartphones and Cloud Storage

2016 ◽  
Author(s):  
Keiko Ochi ◽  
Nobutaka Ono ◽  
Shigeki Miyabe ◽  
Shoji Makino
Keyword(s):  
Speech Recognition ◽  
Blind Source Separation ◽  
Cloud Storage ◽  
Ad Hoc ◽  
Microphone Array ◽  
Source Separation

Acoustical feature extraction of rotating machinery with combined wave superposition and blind source separation

2006 ◽  
Vol 220 (9) ◽  
pp. 1423-1431 ◽  
Author(s):  
W F Xue ◽  
J Chen ◽  
J Q Li ◽  
X F Liu
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Acoustic Radiation ◽  
Microphone Array ◽  
Source Separation ◽  
Point Sources ◽  
Superposition Method ◽  
Sound Sources ◽  
Reconstruction Process ◽  
Wave Superposition

As the result of vibration emission in air, machine sound signal carries affluent information about the working condition of machine and it can be used to make mechanical fault diagnosis. The fundamental problems with fault diagnosis are the estimation of the number of sound sources and the localization of sound sources. The wave superposition can be employed to identify and locate sound sources, which is based on the idea that an acoustic radiator can be approximated and represented by the sum of the fields due to a finite number of interior point sources. But, in practice, a large number of measurements must be used in order to achieve a desired resolution, which makes the reconstruction process very time-consuming and expensive. In this paper, a combined wave superposition method has been developed reconstruct to acoustic radiation from machine acoustical signals. This method combines the advantages of both the wave superposition and Helmholtz equationleast squares methods, and it allows for reconstruction of the acoustic field from an arbitrary object with relatively few measurements, thus significantly enhancing the reconstruction efficiency. After sound source localization, the blind source separation (BSS) is proposed to extract acoustical feature from the mixed measuring sound signals. In a semi-anechoic chamber, a cross-planar microphone array, which consists of 29 microphones, was successfully applied to obtain the two-dimensional mapping of the sound sources. The location, the sound pressure, and the properties in frequency domain of the sound sources can be found through this method precisely. The experimental results demonstrate that the methods presented can potentially become an acoustical diagnosis tool.

Multi-band speech recognition using band-dependent confidence measures of blind source separation

2012 ◽  
Vol 131 (4) ◽  
pp. 3235-3235
Author(s):  
Atsushi Ando ◽  
Hiromasa Ohashi ◽  
Sunao Hara ◽  
Norihide Kitaoka ◽  
Kazuya Takeda
Keyword(s):  
Speech Recognition ◽  
Blind Source Separation ◽  
Source Separation ◽  
Confidence Measures ◽  
Multi Band
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