scholarly journals Evaluating the Benefit of Hearing Aids in Solving the Cocktail Party Problem

2008 ◽  
Vol 12 (4) ◽  
pp. 300-315 ◽  
Author(s):  
Nicole Marrone ◽  
Christine R. Mason ◽  
Gerald Kidd
Author(s):  
Alistair J. Harvey ◽  
C. Philip Beaman

Abstract Rationale To test the notion that alcohol impairs auditory attentional control by reducing the listener’s cognitive capacity. Objectives We examined the effect of alcohol consumption and working memory span on dichotic speech shadowing and the cocktail party effect—the ability to focus on one of many simultaneous speakers yet still detect mention of one’s name amidst the background speech. Alcohol was expected either to increase name detection, by weakening the inhibition of irrelevant speech, or reduce name detection, by restricting auditory attention on to the primary input channel. Low-span participants were expected to show larger drug impairments than high-span counterparts. Methods On completion of the working memory span task, participants (n = 81) were randomly assigned to an alcohol or placebo beverage treatment. After alcohol absorption, they shadowed speech presented to one ear while ignoring the synchronised speech of a different speaker presented to the other. Each participant’s first name was covertly embedded in to-be-ignored speech. Results The “cocktail party effect” was not affected by alcohol or working memory span, though low-span participants made more shadowing errors and recalled fewer words from the primary channel than high-span counterparts. Bayes factors support a null effect of alcohol on the cocktail party phenomenon, on shadowing errors and on memory for either shadowed or ignored speech. Conclusion Findings suggest that an alcoholic beverage producing a moderate level of intoxication (M BAC ≈ 0.08%) neither enhances nor impairs the cocktail party effect.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tim Fischer ◽  
Marco Caversaccio ◽  
Wilhelm Wimmer

AbstractThe Cocktail Party Effect refers to the ability of the human sense of hearing to extract a specific target sound source from a mixture of background noises in complex acoustic scenarios. The ease with which normal hearing people perform this challenging task is in stark contrast to the difficulties that hearing-impaired subjects face in these situations. To help patients with hearing aids and implants, scientists are trying to imitate this ability of human hearing, with modest success so far. To support the scientific community in its efforts, we provide the Bern Cocktail Party (BCP) dataset consisting of 55938 Cocktail Party scenarios recorded from 20 people and a head and torso simulator wearing cochlear implant audio processors. The data were collected in an acoustic chamber with 16 synchronized microphones placed at purposeful positions on the participants’ heads. In addition to the multi-channel audio source and image recordings, the spatial coordinates of the microphone positions were digitized for each participant. Python scripts were provided to facilitate data processing.


2010 ◽  
pp. 61-79 ◽  
Author(s):  
Tariqullah Jan ◽  
Wenwu Wang

Cocktail party problem is a classical scientific problem that has been studied for decades. Humans have remarkable skills in segregating target speech from a complex auditory mixture obtained in a cocktail party environment. Computational modeling for such a mechanism is however extremely challenging. This chapter presents an overview of several recent techniques for the source separation issues associated with this problem, including independent component analysis/blind source separation, computational auditory scene analysis, model-based approaches, non-negative matrix factorization and sparse coding. As an example, a multistage approach for source separation is included. The application areas of cocktail party processing are explored. Potential future research directions are also discussed.


2009 ◽  
Vol 125 (4) ◽  
pp. 2489-2489
Author(s):  
Micheal L. Dent ◽  
Barbara G. Shinn‐Cunningham ◽  
Kamal Sen

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Jayaganesh Swaminathan ◽  
Christine R. Mason ◽  
Timothy M. Streeter ◽  
Virginia Best ◽  
Gerald Kidd, Jr ◽  
...  

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