Two-stage blind source separation based on ICA and binary masking for real-time robot audition system

Saruwatari, Hiroshi; Mori, Yoshimitsu; Takatani, Tomoya; Ukai, Shigeo; Shikano, Kiyohiro; Hiekata, Takashi; Morita, Takashi

doi:10.1109/iros.2005.1544983

Cited by 30 publications

(14 citation statements)

References 13 publications

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“…The distances of the elements of the microphone array d 12 , d 34 were both set to be 250 mm. The sampling frequency of the microphone array was 100 kHz.…”

Section: Conditions Of Experiments Common Conditionmentioning

confidence: 99%

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Self-localization method for mobile robot using acoustic beacons

et al. 2015

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In this paper, we have proposed a low-cost self-localization method which uses 4 elements of microphones, wheel rotation and sound sources as beacons, whose absolute location and frequency bands are known. The proposed method consists of following 4 steps. The proposed method (i) execute self-localization using wheel-based odometry, (ii) estimate direction-of-arrival (DOA) of the sound sources using sounds recorded by the elements of the microphone array, (iii) predict the DOA of the sound sources from estimated location and pose, and (iv) conduct self-localization by integrating all of the information. To evaluate the proposed method, experiments were conducted. The proposed method was compared to the conventional methods, which were wheel-based odometry and self-localization using only DOA. In the experiments, we have supposed the house-cleaning robot and its trajectory. As results, without any obstacles or walls, the mean of the estimation errors by wheel-based odometry were 670 mm and 0.08 rad, and those of self-localization using only DOA were 2870 m and 0.07 rad in the worst case. In contrast with these methods, proposed method results in 69 mm, 0.02 rad as the worst estimation error of self location and pose. From the result with occlusion of a sound source, the mean of the localization error increased 60 mm, as the proposed method detects the incorrect DOA and prevents it from estimation. From the result with reflective wave from wall, there was a place where the localization error was large. The cause of this error was considered as directivity of sound source. These results indicate that the proposed method is feasible under indoor environment.

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“…The distances of the elements of the microphone array d 12 , d 34 were both set to be 250 mm. The sampling frequency of the microphone array was 100 kHz.…”

Section: Conditions Of Experiments Common Conditionmentioning

confidence: 99%

“…Previously, many researches have been done for the use of sound signals for robots and several techniques such as the self-localization, sound sources separation and autonomous speech recognition are reported [8][9][10][11][12][13]. Also there are self-localization methods using microphones installed around the room [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Self-localization method for mobile robot using acoustic beacons

et al. 2015

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“…The two main issues are 1) the design of parallel processing and 2) incremental implementation. The reason we treat the incremental implementation is that MCSB-ICA usually works with batch processing, and the techniques used by Saruwatari et al [3] cannot be applied because the dereverberation mechanism is different from that in ordinary ICA. We cannot adopt the beamformer combination approach such as [4] because their scheme does not consider the reverberation problem which ordinary ICA cannot cope with.…”

Section: B Our Approach and Contributionmentioning

confidence: 99%

Speedup and performance improvement of ICA-based robot audition by parallel and resampling-based block-wise processing

Takeda

Nakadai

Takahashi

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper describes a speedup and performance improvement of multi-channel semi-blind ICA (MCSB-ICA) with parallel and resampling-based block-wise processing. MCSB-ICA is an integrated method of sound source separation that accomplishes blind source separation, blind dereverberation, and echo cancellation. This method enables robots to separate user's speech signals from observed signals including the robot's own speech, other speech and their reverberations without a priori information. The main problem when MCSB-ICA is applied to robot audition is its high computational cost. We tackle this by multi-threading programming, and the two main issues are 1) the design of parallel processing and 2) incremental implementation. These are solved by a) multiplestack-based parallel implementation, and b) resampling-based overlaps and block-wise separation. The experimental results proved that our method reduced the real-time factor to less than 0.5 with an eight-core CPU, and it improves the performance of automatic speech recognition by 2-10 points compared with the single-stack-based parallel implementation without the resampling technique.

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“…Again, reduced signal distortion is accompanied by reduced SNR gain. Saruwatari et al (2005) investigated two different ways of combining binary masking and ICA. A simple way is to generate a binary mask by comparing ICA-separated sources directly.…”

Section: Ica and T-f Maskingmentioning

confidence: 99%

Time-Frequency Masking for Speech Separation and Its Potential for Hearing Aid Design

Wang

2008

Trends in Amplification

143

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A new approach to the separation of speech from speech-in-noise mixtures is the use of time-frequency (T-F) masking. Originated in the field of computational auditory scene analysis, T-F masking performs separation in the time-frequency domain. This article introduces the T-F masking concept and reviews T-F masking algorithms that separate target speech from either monaural or binaural mixtures, as well as microphone-array recordings. The review emphasizes techniques that are promising for hearing aid design. This article also surveys recent studies that evaluate the perceptual effects of T-F masking techniques, particularly their effectiveness in improving human speech recognition in noise. An assessment is made of the potential benefits of T-F masking methods for the hearing impaired in light of the processing constraints of hearing aids. Finally, several issues pertinent to T-F masking are discussed.

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Two-stage blind source separation based on ICA and binary masking for real-time robot audition system

Cited by 30 publications

References 13 publications

Self-localization method for mobile robot using acoustic beacons

Self-localization method for mobile robot using acoustic beacons

Speedup and performance improvement of ICA-based robot audition by parallel and resampling-based block-wise processing

Time-Frequency Masking for Speech Separation and Its Potential for Hearing Aid Design

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