Demonstrations of Underdetermined Blind Separation of Speech Signals

last modified: Jan. 13, 2004.

Contents

1: Abstract
2: References
3: Results (simulated anechoic mixtures)
4: Results (echoic mixtures)
5: Discussions

1: Abstract


Fig. 1: Block diagram of underdetermined BSS.

We propose a method for separating speech signals with little distortion when the signals outnumber the sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks that extract a signal at time points where the number of active sources is estimated to be only one. However, these methods result in an unexpected excess of zero-padding and so the extracted speeches are severely distorted and have loud musical noise. Moreover, the performance depends on the heuristic parameter of mask width.

To overcome this problem, we have proposed combining a binary mask and independent component analysis (ICA). We call this method BMICA. First, using sparseness, we estimate the time points when only one source is active. Then, we remove this single source with a wide binary mask from the observations and apply ICA to the remaining mixtures. Because the single source removal of this method cause less discontinuous zero-padding than in the binary masks only method, we have been able to obtain separated signals with little distortion with our method. However, as our method has still employed a binary mask for one source removal, the zero-padding to the separated signals has still remained. Moreover, there are heuristic parameters to design the shapes of binary masks in the conventional methods.

Here, we propose a new method which employs a directivity pattern based continuous mask (DCmask) in the 1st stage. The DCmask has a small gain for the DOA of one source and preserves signals from other directions. First we estimate the DOA of each sources with sparseness. Then, we remove single source from the observations with a DCmask. Then apply ICA to the remaining mixtures as our former method. Because this DCmask has no zero in their frequency characteristic, discontinuous zero-padding of the extracted signals does not occur by nature. Moreover, we do not need any parameters for designing the DCmask. Experimental results show that our proposed method can separate signals with little distortion even in a reverberant condition without any serious deterioration in the separation performance SIR.

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2: References

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3: Results (simulated anechoic mixtures)

We simulated an omni-directional microphone pair of an inter-element spacing of 4 cm giving some delay to the original speech signals.
The values of delay corresponded to the speech signals from three directions, 45 deg.(s1), 90 deg.(s2), and 135 deg. (s3).
The sampling rate was 8 kHz.
The original speech signals were selected from the ASJ continuous speech corpus.(All speech signals are in Japanese....Sorry...)

In the tables,

In the tables, the values show the separation performance, (SIR, SDR) in dB.
SIR: Signal to Interference Ratio, SDR: Signal to Distortion Ratio.

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4: Results (echoic mixtures)


Fig. 2: Room for echoic tests.

For the echoic tests, we used speech data convolved with impulse responses recorded in a real room (wee Fig.2) whose reverberation time was 130ms.

TR=130ms


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5: We can say that...

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