Underwater Sound Filtering

2015 ◽  
Author(s):  
Ines Simic ◽  
Rutger van Aalst
Keyword(s):  
Blind Source Separation ◽  
Water Waves ◽  
Source Separation ◽  
The Other ◽  
Underwater Sound ◽  
Noisy Environment ◽  
Cocktail Party ◽  
Sound Sources ◽  
Open Sea ◽  
Filtering Problem

The cocktail party algorithm is one of the most widely used algorithms for source separation of sound. The algorithm aims to find an automated solution for a problem that everyone experiences regularly, namely how to make oneself heard in a noisy environment. The cocktail party algorithm picks up the sound from different microphones, and then applies smart filters once the system has determined which sounds originate from the same source. This problem also becomes topical when developing sensors based on passive sonar, for instance for autonomous aquatic drones who have to develop awareness of ships and other possible obstacles on a busy shipping lane. It is possible to deploy multiple hydrophones to localize sound sources under water, but the system will be hindered considerably by the sound that the drone itself makes, such as the sound produced by the propellers. This paper describes a possible solution to the underwater sound filtering problem, using Blind Source Separation. The problem regards splitting sound from a boat engine and the water waves to prove the possibility to extract one sound fragment from the other on the open sea. The illustrations shown further in the report are tests performed in MATLAB to prove the theory.

Blind Source Separation Using a Neural Network

2008 ◽  
Author(s):  
Yukihito Niino ◽  
Toshihiko Shiraishi ◽  
Shin Morishita
Keyword(s):  
Neural Network ◽  
Blind Source Separation ◽  
Sound Source ◽  
Sound Pressure ◽  
Source Separation ◽  
Cocktail Party ◽  
Time Step ◽  
Sound Sources ◽  
The Neural Network ◽  
Cocktail Party Effect

Humans are able to well recognize mixtures of speech signals produced by two or more simultaneous speakers. This ability is known as cocktail party effect. To apply the cocktail party effect to engineering, we can construct novel systems of blind source separation such as current automatic speech recognition systems and active noise control systems under environment noises. A variety of methods have been developed to improve the performance of blind source separation in the presence of background noise or interfering speech. Considering blind source separation as the characteristics of human, artificial neural networks are suitable for it. In this paper, we proposed a method of blind source separation using a neural network. The present neural network can adaptively separate sound sources on training the internal parameters. The network was three-layered. Sound pressure was output from two sound sources and the mixed sound was measured with two microphones. The time history of microphone signals was input to the input layer of neural network. The two outputs of hidden layer were corresponding to the two sound pressure separated respectively. The two outputs of output layer were corresponding to the two microphone signals expected at next time step and compared with the actual microphone signals at next time step to train the neural network by a backpropagation method. In this procedure, the signal from each sound source was adaptively separated. There were two conditions of sound source, sinusoidal signals of 440 and 1000 Hz. In order to assess the performance of neural network numerically and experimentally, a basic independent component analysis (ICA) was conducted simultaneously. The results obtained are as follows. The performance of blind separation by the neural network was higher than the basic ICA. In addition, the neural network can successfully separate the sound source in spite of the position of sound sources.

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.

Frequency domain blind source separation in a noisy environment

2006 ◽  
Vol 120 (5) ◽  
pp. 3045-3045 ◽  
Author(s):  
Ryo Mukai ◽  
Hiroshi Sawada ◽  
Shoko Araki ◽  
Shoji Makino
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Source Separation ◽  
Noisy Environment

BLIND SOURCE SEPARATION OF SPATIO-TEMPORAL MIXED SIGNALS USING PHASE INFORMATION OF ANALYTIC WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 575-594 ◽  
Author(s):  
RYUICHI ASHINO ◽  
TAKESHI MANDAI ◽  
AKIRA MORIMOTO
Keyword(s):  
Wavelet Transform ◽  
Blind Source Separation ◽  
Source Separation ◽  
Cocktail Party ◽  
Phase Information ◽  
Separation Problem ◽  
Time Frequency ◽  
Cocktail Party Problem ◽  
Analytic Wavelet ◽  
Spatio Temporal

The cocktail party problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noises. The blind source separation problem is how to enable computers to solve the cocktail party problem in a satisfactory manner. The simplest version of spatio-temporal mixture problem, which is a type of blind source separation problem, has been solved by a generalized version of the quotient signal estimation method based on the analytic wavelet transform, under the assumption that the time delays are integer multiples of the sampling period. The analytic wavelet transform is used to represent time-frequency information of observed signals. Without the above assumption, improved algorithms, utilizing phase information of the analytic wavelet transforms of the observed signals, are proposed. A series of numerical simulations is presented.

scholarly journals BLIND SOURCE SEPARATION (BSS) APPLIED TO SOUND IN VARIOUS CONDITIONS

2011 ◽  
Vol 14 (4) ◽  
pp. 34-42
Author(s):  
Quang Tan Truong ◽  
Huy Quang Tran ◽  
Phuong Huu Nguyen
Keyword(s):  
Speech Recognition ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Sound Sources ◽  
Sound Separation ◽  
Source Signals

Our ears often simultaneously receive various sound sources (speech, music, noise . . .), but we can still listen to the intended sound. A system of speech recognition must be able to achieve the same intelligent level. The problem is that we receive many mixed (combined) signals from many different source signals, and would like to recover them separately. This is the problem of Blind Source Separation (BSS). In the last decade or so a method has been developed to solve the above problem effectively, that is the Independent Component Analysis (ICA). There are many ICA algorithms for different applications. This report describes our application to sound separation when there are more sources than mixtures (underdetermined case). The results were quite good.

Sign in / Sign up

Export Citation Format

Share Document