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Musical training, individual differences and the cocktail party problem.

ABSTRACT: Are musicians better able to understand speech in noise than non-musicians? Recent findings have produced contradictory results. Here we addressed this question by asking musicians and non-musicians to understand target sentences masked by other sentences presented from different spatial locations, the classical 'cocktail party problem' in speech science. We found that musicians obtained a substantial benefit in this situation, with thresholds ~6?dB better than non-musicians. Large individual differences in performance were noted particularly for the non-musically trained group. Furthermore, in different conditions we manipulated the spatial location and intelligibility of the masking sentences, thus changing the amount of 'informational masking' (IM) while keeping the amount of 'energetic masking' (EM) relatively constant. When the maskers were unintelligible and spatially separated from the target (low in IM), musicians and non-musicians performed comparably. These results suggest that the characteristics of speech maskers and the amount of IM can influence the magnitude of the differences found between musicians and non-musicians in multiple-talker "cocktail party" environments. Furthermore, considering the task in terms of the EM-IM distinction provides a conceptual framework for future behavioral and neuroscientific studies which explore the underlying sensory and cognitive mechanisms contributing to enhanced "speech-in-noise" perception by musicians.

SUBMITTER: Swaminathan J

PROVIDER: S-EPMC4481518 | biostudies-other | 2015 Jun

REPOSITORIES: biostudies-other

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Musical training, individual differences and the cocktail party problem.

Swaminathan Jayaganesh J Mason Christine R CR Streeter Timothy M TM Best Virginia V Kidd Gerald G Patel Aniruddh D AD

Scientific reports 20150626

Are musicians better able to understand speech in noise than non-musicians? Recent findings have produced contradictory results. Here we addressed this question by asking musicians and non-musicians to understand target sentences masked by other sentences presented from different spatial locations, the classical 'cocktail party problem' in speech science. We found that musicians obtained a substantial benefit in this situation, with thresholds ~6 dB better than non-musicians. Large individual di ...[more]

PMID: 26112910

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Project description:The goal of this study was to investigate how cognitive factors influence performance in a multi-talker, "cocktail-party" like environment in musicians and non-musicians. This was achieved by relating performance in a spatial hearing task to cognitive processing abilities assessed using measures of executive function (EF) and visual attention in musicians and non-musicians. For the spatial hearing task, a speech target was presented simultaneously with two intelligible speech maskers that were either colocated with the target (0° azimuth) or were symmetrically separated from the target in azimuth (at ±15°). EF assessment included measures of cognitive flexibility, inhibition control and auditory working memory. Selective attention was assessed in the visual domain using a multiple object tracking task (MOT). For the MOT task, the observers were required to track target dots (n = 1,2,3,4,5) in the presence of interfering distractor dots. Musicians performed significantly better than non-musicians in the spatial hearing task. For the EF measures, musicians showed better performance on measures of auditory working memory compared to non-musicians. Furthermore, across all individuals, a significant correlation was observed between performance on the spatial hearing task and measures of auditory working memory. This result suggests that individual differences in performance in a cocktail party-like environment may depend in part on cognitive factors such as auditory working memory. Performance in the MOT task did not differ between groups. However, across all individuals, a significant correlation was found between performance in the MOT and spatial hearing tasks. A stepwise multiple regression analysis revealed that musicianship and performance on the MOT task significantly predicted performance on the spatial hearing task. Overall, these findings confirm the relationship between musicianship and cognitive factors including domain-general selective attention and working memory in solving the "cocktail party problem".

Project description:The human auditory system has the ability to segregate complex auditory scenes into a foreground component and a background, allowing us to listen to specific speech sounds from a mixture of sounds. Selective attention plays a crucial role in this process, colloquially known as the "cocktail party effect." It has not been possible to build a machine that can emulate this human ability in real-time. Here, we have developed a framework for the implementation of a neuromorphic sound segregation algorithm in a Field Programmable Gate Array (FPGA). This algorithm is based on the principles of temporal coherence and uses an attention signal to separate a target sound stream from background noise. Temporal coherence implies that auditory features belonging to the same sound source are coherently modulated and evoke highly correlated neural response patterns. The basis for this form of sound segregation is that responses from pairs of channels that are strongly positively correlated belong to the same stream, while channels that are uncorrelated or anti-correlated belong to different streams. In our framework, we have used a neuromorphic cochlea as a frontend sound analyser to extract spatial information of the sound input, which then passes through band pass filters that extract the sound envelope at various modulation rates. Further stages include feature extraction and mask generation, which is finally used to reconstruct the targeted sound. Using sample tonal and speech mixtures, we show that our FPGA architecture is able to segregate sound sources in real-time. The accuracy of segregation is indicated by the high signal-to-noise ratio (SNR) of the segregated stream (90, 77, and 55 dB for simple tone, complex tone, and speech, respectively) as compared to the SNR of the mixture waveform (0 dB). This system may be easily extended for the segregation of complex speech signals, and may thus find various applications in electronic devices such as for sound segregation and speech recognition.

Dataset Information

Musical training, individual differences and the cocktail party problem.

Publications

Musical training, individual differences and the cocktail party problem.

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