Design and Implementation of Robot Audition System 'HARK' — Open Source Software for Listening to Three Simultaneous Speakers

Nakadai, Kazuhiro; Takahashi, Tôru; Okuno, Hiroshi G.; Nakajima, Hironori; Hasegawa, Yuji; Tsujino, Hiroshi

doi:10.1163/016918610x493561

Cited by 190 publications

(75 citation statements)

References 9 publications

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“…For instance, HARK [7] localizes the sound sources before separation by using the number of sources. When independent component analysis (ICA), a well-known BSS method, is applied to the input signals, principal component analysis (PCA) is commonly used as preprocessing for ICA [8].…”

Section: Robust Estimation Without Source Knowledgementioning

confidence: 99%

“…Many source separation frameworks use a cascaded approach. For instance, HARK [7] localizes the sources first and then separates the observed signals into individual sources; the conventional frequency-domain ICA separates the observations and then resolves the permutation problem. One of the critical weak points of these cascaded approaches is that the separation performance is limited to the performance of the worst subprocess.…”

Section: Unified Approachmentioning

confidence: 99%

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Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

Nagira

Otsuka

Okuno

2013

J AUDIO SPEECH MUSIC PROC.

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Blind source separation (BSS) and sound activity detection (SAD) from a sound source mixture with minimum prior information are two major requirements for computational auditory scene analysis that recognizes auditory events in many environments. In daily environments, BSS suffers from many problems such as reverberation, a permutation problem in frequency-domain processing, and uncertainty about the number of sources in the observed mixture. While many conventional BSS methods resort to a cascaded combination of subprocesses, e.g., frequency-wise separation and permutation resolution, to overcome these problems, their outcomes may be affected by the worst subprocess. Our aim is to develop a unified framework to cope with these problems. Our method, called permutationfree infinite sparse factor analysis (PF-ISFA), is based on a nonparametric Bayesian framework that enables inference without a pre-determined number of sources. It solves BSS, SAD and the permutation problem at the same time. Our method has two key ideas: unified source activities for all the frequency bins and the activation probabilities of all the frequency bins of all the sources. Experiments were carried out to evaluate the separation performance and the SAD performance under four reverberant conditions. For separation performance in the BSS EVAL criteria, our method outperformed conventional complex ISFA under all conditions. For SAD performance, our method outperformed the conventional method by 5.9-0.5% in F-measure under the condition RT 20 = 30-600 [ms], respectively.

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Section: Robust Estimation Without Source Knowledgementioning

confidence: 99%

Section: Unified Approachmentioning

confidence: 99%

Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

Nagira

Otsuka

Okuno

2013

J AUDIO SPEECH MUSIC PROC.

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“…Finally, we derive the ASR performance with re-sampling block-wise processing, and compare our method with that of other state-of-the-art robot audition software, HARK [11], as a baseline method. In block-wise processing, we selected three patterns for the parameters, i.e., is the maximum latency permissible by ASR.…”

Section: Items For Evaluationmentioning

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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“…A cursory reading of the literature in robot audition can lead one to the works of well-known robot audition research groups/ projects such as HARK [2], ManyEars from IntRoLab [3], BINAARH [4] and its consequent EAR project [5]. However, other than these efforts, the rest of the works are mainly carried out by robotics research groups that seem to see robot audition as an "additional" or "optional" part of their robotic solutions.…”

Section: Introductionmentioning

confidence: 99%

Robots that Hear

Rascón

2017

IRATJ

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Design and Implementation of Robot Audition System 'HARK' — Open Source Software for Listening to Three Simultaneous Speakers

Cited by 190 publications

References 9 publications

Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

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

Robots that Hear

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