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Treefrogs as animal models for research on auditory scene analysis and the cocktail party problem.

ABSTRACT: The perceptual analysis of acoustic scenes involves binding together sounds from the same source and separating them from other sounds in the environment. In large social groups, listeners experience increased difficulty performing these tasks due to high noise levels and interference from the concurrent signals of multiple individuals. While a substantial body of literature on these issues pertains to human hearing and speech communication, few studies have investigated how nonhuman animals may be evolutionarily adapted to solve biologically analogous communication problems. Here, I review recent and ongoing work aimed at testing hypotheses about perceptual mechanisms that enable treefrogs in the genus Hyla to communicate vocally in noisy, multi-source social environments. After briefly introducing the genus and the methods used to study hearing in frogs, I outline several functional constraints on communication posed by the acoustic environment of breeding "choruses". Then, I review studies of sound source perception aimed at uncovering how treefrog listeners may be adapted to cope with these constraints. Specifically, this review covers research on the acoustic cues used in sequential and simultaneous auditory grouping, spatial release from masking, and dip listening. Throughout the paper, I attempt to illustrate how broad-scale, comparative studies of carefully considered animal models may ultimately reveal an evolutionary diversity of underlying mechanisms for solving cocktail-party-like problems in communication.

SUBMITTER: Bee MA

PROVIDER: S-EPMC4096220 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Treefrogs as animal models for research on auditory scene analysis and the cocktail party problem.

Bee Mark A MA

International journal of psychophysiology : official journal of the International Organization of Psychophysiology 20140111 2

The perceptual analysis of acoustic scenes involves binding together sounds from the same source and separating them from other sounds in the environment. In large social groups, listeners experience increased difficulty performing these tasks due to high noise levels and interference from the concurrent signals of multiple individuals. While a substantial body of literature on these issues pertains to human hearing and speech communication, few studies have investigated how nonhuman animals may ...[more]

PMID: 24424243

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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

Treefrogs as animal models for research on auditory scene analysis and the cocktail party problem.

Publications

Treefrogs as animal models for research on auditory scene analysis and the cocktail party problem.

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