Computational auditory scene analysis: A representational approach

Brown, Guy J.

doi:10.1121/1.407441

Cited by 41 publications

(47 citation statements)

References 192 publications

(332 reference statements)

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“…No prior knowledge or learned pattern is involved. system harmonicity continuity onset offset AM FM Weintraub (Weintraub, 1985) Cooke (Cooke, 1993) Mellinger (Mellinger, 1991) Brown (Brown, 1992) The study of Brown presents a segregation system that improves from previous systems (Brown, 1992;Brown & Cooke, 1994;. It is aimed to segregate speech from an arbitrary intrusion, such as narrow-band noise, siren and concurrent speech source.…”

Section: Single-microphone Speech Separationmentioning

confidence: 99%

“…Comparing with a conventional recognition system, a significant reduction in word error rate is achieved. Schema-based separation is an emergent and potential direction that research work on separation algorithm is less explored, compared to systems using primitive cues alone (Barker et al, 2005;2006;Brown, 1992;Brown & Cooke, 1994;Cooke, 1993;Ellis, 1996;Hu & Wang, 2006). Traditional CASA separation systems rely on the use of primitive cues in a data-driven manner, where low-level acoustic features are gradually merged to form the resultant scenes.…”

Section: Single-microphone Speech Separationmentioning

confidence: 99%

“…Shortly after the publication of the CASA tome from Bregman, a series of separation systems for speech sources or music emerged (Brown, 1992;Cooke, 1993;Ellis, 1994;Mellinger, 1991). All of them differed from previous systems in a way that a number of primitive grouping cues are adopted, rather than exploiting harmonicity alone.…”

Section: Single-microphone Speech Separationmentioning

confidence: 99%

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Single-Microphone Speech Separation: The use of Speech Models

Lee¹

2011

Speech Technologies

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Section: Single-microphone Speech Separationmentioning

confidence: 99%

Section: Single-microphone Speech Separationmentioning

confidence: 99%

Section: Single-microphone Speech Separationmentioning

confidence: 99%

See 1 more Smart Citation

Single-Microphone Speech Separation: The use of Speech Models

Lee¹

2011

Speech Technologies

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“…The reconciliation engine, which updates the worldmodel in response to errors reported between the observed and predicted signals, is the most critical part The principle modules of data-driven sound organization (upper panel) such as [Brown 1992] contrasted with the predictiondriven approach (lower panel) described in this paper.…”

Section: The Prediction-driven Architecturementioning

confidence: 99%

Using knowledge to organize sound: The prediction-driven approach to computational auditory scene analysis and its application to speech/nonspeech mixtures

Ellis

1999

Speech Communication

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Computational auditory scene analysis -modeling the human ability to organize sound mixtures according to their sources -has experienced a rapid evolution as the simple principles suggested by psychological experiments have turned out to be less than the whole story. Phenomena such as the continuity illusion and phonemic restoration show that the brain is able to use a wide range of knowledge-based contextual constraints when interpreting obscured or complex mixtures: To model such processing, we need architectures that operate by confirming hypotheses about the observations rather than relying on directly-extracted descriptions. One such architecture, the 'prediction-driven' approach, is presented along with results from its initial implementation. This architecture can be extended to take advantage of the high-level knowledge implicit in today's speech recognizers by modifying a recognizer to act as one of the 'component models' which provide the explanations of the signal mixture. Although this adaptation raises a number of issues, a preliminary investigation supports the argument that successful scene analysis must exploit such abstract knowledge at every level. IntroductionThe work described in this paper fits into a kind of evolutionary tale of approaches to sound organization: In the beginning, there was the 'simplistic' or "blank background" view that sound objects somehow defined themselves, and that identifying a single perceptual object was as simple as picking out a figure in a child's coloring book. The experimental stimuli on which so much of our understanding of auditory organization is based -the sinusoids and bandlimited noise bursts of Bregman [1990] and others -echo this approach, since, as presented in soundproof listening booths, they would actually be amenable to such an approach.The second stage of evolution, which we might call the 'optimistic' or "uniform background" view, emerged from the initial efforts to apply the insights of experimental results in auditory organization (especially those in [Bregman 1990]) and apply them to real sounds.Unlike sinusoids against a silent background, real sounds contain all kinds of noise and distractions to defeat simple extraction routines, and therefore demand a more sophisticated approach. However, the signal processing community is long used to dealing with noise and offers various approaches for making the best possible decisions under some simple, but useful, assumptions. These amount to a kind of template matching, such that if the form of the target and the interference can be exactly specified, the parameters of the target can be recovered in the mathematically best-possible fashion. The essence of this approach is that we can produce simple definitions of what we are looking for -sinusoids of unknown frequency, or narrowband noise energy -and we can then go through a given signal identifying and extracting just the parts that interest us, and ignoring the rest -in analogy to the way a human listener is able to 'screen out' interferin...

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“…As traditional auditory research attempts to understand such psychological phenomenon, Computational Auditory Scene Analysis (CASA) studies a general framework of sound processing and understanding [4,5,13,16]. Its goal is to understand an arbitrary sound mixture including speech, non-speech sounds, and music in various acoustic environment.…”

Section: Introductionmentioning

confidence: 99%

Active audition system and humanoid exterior design

Nakadai¹,

Matsui

Okuno

et al.

Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113)

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In this paper, we present that a humanoid active audition system improved noise cancellation using humanoid cover acoustics and the cover design for an industrial exterior design.From the viewpoint of a design, the cover of humanoid should be functional and beautiful. And, the humanoid exterior should not obstruct the motion of the humanoid. In other words, a humanoid with motion requires design for motion. This is an important issue in the robotics eld because exterior design of robots has not been suciently studied yet.As for active audition, it is an important problem to distinguish between the internal sounds like motor noises and sounds which originate from the outer world. It is important that inevitable motor noise is cancelled while the humanoid is in motion. Therefore, humanoids require the exterior to cancel such internal noises because it separates humanoid inner world from the outer world.We reported an active audition system focused o n the sound source t r acking by integrating audition, vision and motor movements using sound separation of the humanoid cover.The experiments show that the humanoid can track and localize sound sources more a c curately, however noise cannot always cancelled out optimally and further improvements are r equired.

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Computational auditory scene analysis: A representational approach

Cited by 41 publications

References 192 publications

Single-Microphone Speech Separation: The use of Speech Models

Single-Microphone Speech Separation: The use of Speech Models

Using knowledge to organize sound: The prediction-driven approach to computational auditory scene analysis and its application to speech/nonspeech mixtures

Active audition system and humanoid exterior design

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