Modeling the Cocktail Party Problem

Elhilali, Mounya

doi:10.1007/978-3-319-51662-2_5

Cited by 8 publications

(5 citation statements)

References 109 publications

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“…Indeed, models of auditory scene analysis are now extending beyond simple scenes composed of tones and sparse sound patterns to more complex and challenging scenarios (e.g., concurrent speakers in noisy, natural environments (for review, see Ref. )). Many such models are now being compared, and some even outperform state‐of‐the‐art systems developed using pure engineering principles that are tailored to specific applications using extensive training data …”

Section: Discussionmentioning

confidence: 99%

Recent advances in exploring the neural underpinnings of auditory scene perception

Snyder

Elhilali

2017

Annals of the New York Academy of Sciences

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Studies of auditory scene analysis have traditionally relied on paradigms using artificial sounds—and conventional behavioral techniques—to elucidate how we perceptually segregate auditory objects or streams from each other. In the past few decades, however, there has been growing interest in uncovering the neural underpinnings of auditory segregation using human and animal neuroscience techniques, as well as computational modeling. This largely reflects the growth in the fields of cognitive neuroscience and computational neuroscience and has led to new theories of how the auditory system segregates sounds in complex arrays. The current review focuses on neural and computational studies of auditory scene perception published in the past few years. Following the progress that has been made in these studies, we describe (1) theoretical advances in our understanding of the most well-studied aspects of auditory scene perception, namely segregation of sequential patterns of sounds and concurrently presented sounds; (2) the diversification of topics and paradigms that have been investigated; and (3) how new neuroscience techniques (including invasive neurophysiology in awake humans, genotyping, and brain stimulation) have been used in this field.

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Section: Discussionmentioning

confidence: 99%

Recent advances in exploring the neural underpinnings of auditory scene perception

Snyder

Elhilali

2017

Annals of the New York Academy of Sciences

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“…Rather than providing a default medium setting offering a compromise in terms of directionality and noise reduction, the four programs represent contrasting aspects of omnidirectionality, brightness and noise reduction. Assessing which programs are preferred, making it possible to assess how users apply aspects of omnidirectionality or noise removal, to spatially differentiate auditory streams, which is essential in order to cognitively separate and selectively attend to competing voices or interfering sounds [31]. There were three dimensions altered in this experiment: brightness and noise reduction, coupled with attenuation.…”

Section: Methodsmentioning

confidence: 99%

Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data

et al. 2017

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The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of the leading lifestyle related causes of dementia and cognitive decline. Taking a radically different approach to personalize the fitting process of hearing aids, by learning contextual preferences from user-generated data, we in this paper outline the results obtained through a 9-month pilot study. Empowering the user to select between several settings using Internet of things (IoT) connected hearing aids allows for modeling individual preferences and thereby identifying distinct coping strategies. These behavioral patterns indicate that users prefer to switch between highly contrasting aspects of omnidirectionality and noise reduction dependent on the context, rather than relying on the medium "one size fits all" program frequently provided by default in hearing health care. We argue that an IoT approach facilitated by the usage of smartphones may constitute a paradigm shift, enabling continuous personalization of settings dependent on the changing context. Furthermore, making the user an active part of the fitting solution based on self-tracking may increase engagement and awareness and thus improve the quality of life for hearing impaired users. reasons for not using a hearing aid were that the devices did not provide sufficient benefits in noisy situations and there was a perceived poor quality of sound [9].One may ask, why is it that people do not choose to use hearing aids, given the evidence of a high risk of incident dementia, and knowing that these could potentially alleviate cognitive decline? Studies analyzing outcome measures capturing the user satisfaction indicate that this is largely determined by two factors: (1) whether the user perceives an improved quality of life through use of the devices, and (2) to what degree they help overcome limitations when interacting with others around the user. The degree to which the user feels involved in the traditional clinical fitting process highly impacts the overall satisfaction [10]. Alternative models for selling hearing aids over the counter based on do-it-yourself audiometry tests may technically provide the same fitting as provided in a clinical setting [11]. However, the lack of dialogue and hearing care counseling has been shown to result in lower satisfaction. Actively involving the user in shaping the listening experience when adapting to the devices appears to be crucial.

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“…In doing so, these models effectively capture the inter-dependencies between object attributes and learn their mapping onto an integrated representational space [14–16]. Ultimately, success in tackling scene analysis depends on two key components [17]: (i) obtaining a rich and robust feature representation that can capture object specific details present in the scene; (ii) grouping the feature elements such that their spatial and temporal associations match the dynamics of objects within the scene.…”

Section: Introductionmentioning

confidence: 99%

“…In audition, computational approaches to tackle auditory scene organization have mostly taken advantage of physiological and perceptual underpinnings of sound processing [17]. A large body of work has built on knowledge of the auditory pathway, particularly the peripheral system to build sophisticated analysis models of auditory scenes.…”

Section: Introductionmentioning

confidence: 99%

A Gestalt inference model for auditory scene segregation

Chakrabarty

Elhilali

2019

PLoS Comput Biol

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Our current understanding of how the brain segregates auditory scenes into meaningful objects is in line with a Gestaltism framework. These Gestalt principles suggest a theory of how different attributes of the soundscape are extracted then bound together into separate groups that reflect different objects or streams present in the scene. These cues are thought to reflect the underlying statistical structure of natural sounds in a similar way that statistics of natural images are closely linked to the principles that guide figure-ground segregation and object segmentation in vision. In the present study, we leverage inference in stochastic neural networks to learn emergent grouping cues directly from natural soundscapes including speech, music and sounds in nature. The model learns a hierarchy of local and global spectro-temporal attributes reminiscent of simultaneous and sequential Gestalt cues that underlie the organization of auditory scenes. These mappings operate at multiple time scales to analyze an incoming complex scene and are then fused using a Hebbian network that binds together coherent features into perceptually-segregated auditory objects. The proposed architecture successfully emulates a wide range of well established auditory scene segregation phenomena and quantifies the complimentary role of segregation and binding cues in driving auditory scene segregation.

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Modeling the Cocktail Party Problem

Cited by 8 publications

References 109 publications

Recent advances in exploring the neural underpinnings of auditory scene perception

Recent advances in exploring the neural underpinnings of auditory scene perception

Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data

A Gestalt inference model for auditory scene segregation

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