Iain Jackson

Social cognition in infancy: A critical review of research on higher order abilities

This paper reviews early precursors to social cognition in infancy, then critically reviews infancy work suggesting goal attribution to human agents in the first year of life and theory of mind (ToM) abilities (assessed through a modified false belief task) in the second year of life. Overall, methodological problems and statistical limitations compound data interpretation, which would be equivocal despite these limitations. The authors find no support for high-order social cognitive abilities in infancy. The discussion focuses on how the field of social cognition in infancy should build theories from the bottom up, assessing how simpler precursors change over time and combine to give rise to socially competent individuals.

Perception and automated assessment of audio quality in user generated content: An improved model

Technology to record sound, available in personal devices such as smartphones or video recording devices, is now ubiquitous. However, the production quality of the sound on this user-generated content is often very poor: distorted, noisy, with garbled speech or indistinct music. Our interest lies in the causes of the poor recording, especially what happens between the sound source and the electronic signal emerging from the microphone, and finding an automated method to warn the user of such problems. Typical problems, such as distortion, wind noise, microphone handling noise and frequency response, were tested. A perceptual model has been developed from subjective tests on the perceived quality of such errors and data measured from a training dataset composed of various audio files. It is shown that perceived quality is associated with distortion and frequency response, with wind and handling noise being just slightly less important. In addition, the contextual content of the audio sample was found to modulate perceived quality at similar levels to degradations such as wind and rendering those introduced by handling noise negligible.

Perception and automatic detection of wind-induced microphone noise

Wind can induce noise on microphones, causing problems for users of hearing aids and for those making recordings outdoors. Perceptual tests in the laboratory and via the Internet were carried out to understand what features of wind noise are important to the perceived audio quality of speech recordings. The average A-weighted sound pressure level of the wind noise was found to dominate the perceived degradation of quality, while gustiness was mostly unimportant. Large degradations in quality were observed when the signal to noise ratio was lower than about 15 dB. A model to allow an estimation of wind noise level was developed using an ensemble of decision trees. The model was designed to work with a single microphone in the presence of a variety of foreground sounds. The model outputted four classes of wind noise: none, low, medium, and high. Wind free examples were accurately identified in 79% of cases. For the three classes with noise present, on average 93% of samples were correctly assigned. A second ensemble of decision trees was used to estimate the signal to noise ratio and thereby infer the perceived degradation caused by wind noise.

Wind-induced microphone noise detection - automatically monitoring the audio quality of field recordings

Wind-induced microphone noise is one of the most common problems leading to poor audio quality in recordings. A wind-noise detector could alert the operator of a recording device to the presence of wind noise so that appropriate action can be taken. This paper presents a single channel algorithm which, within the presence of other sounds, detects and classifies wind noise according to level. A large training database is formed from a wind noise simulator which generates an audio stream based on time histories of real wind velocities. A Support Vector Machine detects and classifies according to wind noise level in 25 ms frames which may contain other sounds. Statistical and temporal data from the detector over a sequence of frames is then used to provide estimates for the average wind noise level. The detector is successfully demonstrated on a number of devices with non-simulated data.

Pupil dilation and infant cognition

Looking time data provide the main window into early cognitive abilities of human infants. With respect to advanced conceptual capabilities, research findings supporting such skills in infants using Violation-of-Expectations (VOE) methods have been met with sustained criticism. This paper proposes the introduce pupil dilation data as a complement to looking time measures. Pupil dilation is an autonomous response to arousal, and should be observed if and when infants are surprised by conceptually unusual events, as typically used in VOE tasks. This paper reports on analyses of pupil dilation data to VOE events involving an implausible change of object identity. The data show that while some aspects of pupil responses show fluctuations across trials, some important features that help in the interpretation looking time data remain stable. The discussion stresses the potential benefits of including pupil data analyses in the study of infant cognition.

Microphone Handling Noise: Measurements of Perceptual Threshold and Effects on Audio Quality.

A psychoacoustic experiment was carried out to test the effects of microphone handling noise on perceived audio quality. Handling noise is a problem affecting both amateurs using their smartphones and cameras, as well as professionals using separate microphones and digital recorders. The noises used for the tests were measured from a variety of devices, including smartphones, laptops and handheld microphones. The signal features that characterise these noises are analysed and presented. The sounds include various types of transient, impact noises created by tapping or knocking devices, as well as more sustained sounds caused by rubbing. During the perceptual tests, listeners auditioned speech podcasts and were asked to rate the degradation of any unwanted sounds they heard. A representative design test methodology was developed that tried to encourage everyday rather than analytical listening. Signal-to-noise ratio (SNR) of the handling noise events was shown to be the best predictor of quality degradation. Other factors such as noise type or background noise in the listening environment did not significantly affect quality ratings. Podcast, microphone type and reproduction equipment were found to be significant but only to a small extent. A model allowing the prediction of degradation from the SNR is presented. The SNR threshold at which 50% of subjects noticed handling noise was found to be 4.2 ± 0.6 dBA. The results from this work are important for the understanding of our perception of impact sound and resonant noises in recordings, and will inform the future development of an automated predictor of quality for handling noise.

Automatic detection of microphone handling noise

Microphone handling noise is a common problem with user-generated content. It can occur when the operator inadvertently knocks or brushes a recording device. Handling noise may be impulsive, where a microphone is knocked, or a more sustained rubbing noise, when the microphone is brushed against something. A detector able to accurately detect handling noises caused by rubbing while recording speech, music or quotidian sounds has been developed. Ensembles of decision trees were trained to classify handling noise level over 23 ms frames; a second ensemble flags frames when the noise may be masked by foreground audio. Aggregation of the detection over 1 s yielded a Matthews correlation coefficient of 0.91.

Perceptual evaluation of the functional and aesthetic degradation of speech by wind noise during recording

This paper will present results from a systematic investigation into functional and aesthetic audio quality of speech recordings degraded by wind noise. The major source of wind noise tested comes from velocity fluctuations interacting with the transducer, generating pressure fluctuations at the microphone diaphragm. To better understand the effect of this type of noise, a perceptual experiment was designed to assess task performance and perceptions of quality when speech and simulated wind noise are presented together. A wind noise simulator was developed, which produces realistic audio from anemometer data, to allow the noise to be isolated from other ambient sounds, and also enable salient parameters to be controlled. Two key components of wind noise in recordings were evaluated, the average level and its temporal variance or “gustiness.” Eight levels of wind noise were factorially combined with three levels of gustiness. Each of these permutations was then presented with one of 24 randomly assigned, g...