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Sound segregation via embedded repetition is robust to inattention.

ABSTRACT: The segregation of sound sources from the mixture of sounds that enters the ear is a core capacity of human hearing, but the extent to which this process is dependent on attention remains unclear. This study investigated the effect of attention on the ability to segregate sounds via repetition. We utilized a dual task design in which stimuli to be segregated were presented along with stimuli for a "decoy" task that required continuous monitoring. The task to assess segregation presented a target sound 10 times in a row, each time concurrent with a different distractor sound. McDermott, Wrobleski, and Oxenham (2011) demonstrated that repetition causes the target sound to be segregated from the distractors. Segregation was queried by asking listeners whether a subsequent probe sound was identical to the target. A control task presented similar stimuli but probed discrimination without engaging segregation processes. We present results from 3 different decoy tasks: a visual multiple object tracking task, a rapid serial visual presentation (RSVP) digit encoding task, and a demanding auditory monitoring task. Load was manipulated by using high- and low-demand versions of each decoy task. The data provide converging evidence of a small effect of attention that is nonspecific, in that it affected the segregation and control tasks to a similar extent. In all cases, segregation performance remained high despite the presence of a concurrent, objectively demanding decoy task. The results suggest that repetition-based segregation is robust to inattention.

SUBMITTER: Masutomi K

PROVIDER: S-EPMC4763252 | biostudies-other | 2016 Mar

REPOSITORIES: biostudies-other

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Project description:Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 ms) and long-term (30 min) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that PCEN reduces temporal overfitting across dawn vs. dusk audio clips whereas context adaptation on PCEN-based summary statistics reduces spatial overfitting across sensor locations. Moreover, combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.

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Sound segregation via embedded repetition is robust to inattention.

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